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针对高通在近似计算的研究分析
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Could you please clarify what aspects of Qualcomm's research into approximate computing you're most interested in? For example:
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Specific applications (e.g., mobile vision, AI acceleration, DSP, etc.)
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Types of approximate computing (e.g., hardware-level approximation, neural approximators, voltage overscaling, etc.)
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Time range (e.g., recent publications/patents in the last 5 years?)
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Desired output format (e.g., structured report, technology timeline, patent-to-product mapping?)
This will help me tailor the research to your focus, especially within handheld imaging or edge AI domains.
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以你为主
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您说：
以你为主
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Thanks! To provide the most valuable analysis, could you confirm if you'd like me to focus on Qualcomm’s research into approximate computing specifically within:
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Handheld imaging and edge AI (e.g., smartphone camera pipelines, real-time vision inference)?
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General-purpose AI accelerators (e.g., Hexagon DSP, NPU architectures)?
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Low-power embedded systems (e.g., IoT or wearable devices)?
And would you like an emphasis on:
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Academic papers
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Patents
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Commercial product integration
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Or a mix of all?
This will help me go as deep and precise as possible.
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all
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Great. I will conduct a deep-dive into Qualcomm's research and development in approximate computing across all relevant domains—including handheld imaging, edge AI, general-purpose AI accelerators, and low-power embedded systems. I will explore academic publications, patents, and any commercial product integrations to uncover key innovations, trends, and implications. I'll let you know as soon as the analysis is ready.
Qualcomm’s R&D in Approximate Computing: A Comprehensive Analysis
Introduction
Approximate computing (AxC) is the design paradigm of trading off computational accuracy for gains in efficiency (power, speed, or area). It exploits the error tolerance of many applications – especially in multimedia, machine learning, and sensor processing – to relax precision requirements while still delivering acceptable results
. Qualcomm’s research and development over the past decade reflects a strong emphasis on approximate computing across several domains. From mobile imaging pipelines to AI accelerators and ultra-low-power IoT chips, Qualcomm has explored a range of algorithmic and architectural strategies (voltage/frequency over-scaling, precision scaling, approximate memory, neural approximators, hardware/software co-design, etc.) to push the envelope of energy-efficient computing. In this report, we survey Qualcomm’s peer-reviewed publications, patents, and product features related to approximate computing, highlighting key techniques and how they map into commercial platforms.Approximate Computing in Handheld Imaging and Edge AI
Modern smartphone cameras and on-device vision AI tasks present an ideal use-case for approximate computing: they handle high-volume sensor data under tight power and latency budgets, and outputs (images or detected objects) need only be perceptually or statistically correct rather than bit-exact.現代智慧手機攝像頭和設備端視覺 AI 任務為近似計算提供了理想的用例：它們在緊湊的功耗和延遲預算下處理大量感測器數據，並且輸出（圖像或檢測到的物體）只需要在感知或統計上正確，而不是精確。
Algorithmic Approximations in Image Pipelines: Qualcomm’s Snapdragon platforms include the Spectra™ Image Signal Processor (ISP), which performs complex imaging tasks (noise reduction, HDR blending, etc.) in real-time. Many of these tasks use approximation for efficiency. For example, recent Qualcomm patents describe adaptive computation skipping in video processing – if global motion between frames is small, the ISP can skip local motion estimation steps to save power
. The ISP instead uses an inexpensive global transform, which approximates the result of full motion compensation with much less computation. Another patent on multi-frame image fusion shows the ISP applying tone-mapping to “map one set of colors to another… to approximate the appearance of a high-dynamic-range image” – essentially a fast, heuristic HDR simulation that trades some fidelity for speed. These examples illustrate Qualcomm’s use of algorithm-level approximation (skipping or simplifying processing) in camera pipelines to meet real-time constraints.Image Pipelines 中的演算法近似值：Qualcomm 的 Snapdragon 平臺包括 Spectra™ 圖像信號處理器 （ISP），它可以即時執行複雜的成像任務（降噪、HDR 混合等）。其中許多任務使用近似來提高效率。例如，最近的 Qualcomm 專利描述了視頻處理中的自適應計算跳躍 ——如果幀之間的全域運動很小，ISP 可以跳過局部運動估計步驟以節省 powerpatent.nweon.com。ISP 使用廉價的全域變換，它以更少的 computationpatent.nweon.compatent.nweon.com 近似於完全運動補償的結果。另一項關於多幀圖像融合的專利顯示 ISP 應用色調映射來「將一組顏色映射到另一組顏色......近似高動態範圍圖像的外觀 patents.google.compatents.google.com – 本質上是一種快速的啟發式 HDR 類比，它以一些保真度換取速度。這些示例說明瞭 Qualcomm 在攝像機管道中使用演算法級近似（跳過或簡化處理）來滿足即時約束。Edge Vision Inference: On-device AI for camera applications (e.g. object detection in video) is another arena where Qualcomm employs AxC. A notable Qualcomm-led research is Delta Distillation (Habibian et al., ECCV 2022), an approach to speed up video neural networks by approximating per-frame processing
. Instead of running a heavy CNN on every frame, the first frame is processed fully and subsequent frames are represented as deltas (differences) which are passed through a much smaller network. This student network learns to predict the change in features/frame outputs relative to a reference frame, via knowledge distillation from a teacher network. The result is that redundant information in temporally adjacent frames is not recomputed from scratch – a form of temporal approximation exploiting video redundancy. This technique, developed by Qualcomm AI Research in collaboration with academia, yielded significant speedups on video tasks with minimal accuracy loss. It reflects a broader strategy of neural network approximators for vision: using smaller or partial models to approximate the output of a larger model on “easy” or redundant inputs (like consecutive video frames). Another recent effort is Clockwork Diffusion (Qualcomm AI Research 2023), which targets generative vision models. It observes that not all denoising steps in a diffusion model influence output equally; by reusing computation from previous steps for low-resolution feature maps, later steps can use an approximate cached result. This yielded ~32% FLOP savings on Stable Diffusion with virtually no loss in output quality. Both Delta Distillation and Clockwork Diffusion demonstrate Qualcomm’s algorithm-level innovations: they exploit temporal or iterative redundancy to avoid repeat work, using approximation (deltas or cached features) to cut down computation while maintaining visual fidelity.邊緣視覺推理： 用於攝像頭應用的設備端 AI（例如視頻中的物件檢測）是 Qualcomm 使用 AxC 的另一個領域。高通公司領導的一項著名研究是 Delta Distillation（Habibian 等人 ，ECCV 2022），這是一種通過近似每幀 processinggithub.comgithub.com 來加速視頻神經網路的方法。第一幀被完全處理，後續幀表示為增量 （差異），通過更小的網路傳遞，而不是在每幀上運行繁重的 CNN。這個學生網路通過從教師 networkgithub.comgithub.com 中提煉知識來學習預測特徵/框架輸出相對於參考框架的變化。結果是時間相鄰幀中的冗餘資訊不會從頭開始重新計算——這是一種利用視頻冗餘的時間近似形式。這項技術由 Qualcomm AI Research 與學術界合作開發，以最低的準確率 lossgithub.comgithub.com 顯著提高了視頻任務的速度。它反映了用於視覺的神經網路近似器的更廣泛策略：使用較小或部分模型來近似較大模型在“簡單”或冗餘輸入（如連續視頻幀）上的輸出。最近的另一項工作是 Clockwork Diffusion（Qualcomm AI Research 2023），它針對生成式視覺模型。 它觀察到，並非擴散模型中的所有降噪步驟對輸出的影響都相同;通過將前面步驟中的計算重複用於低解析度特徵圖，後面的步驟可以使用近似緩存的結果 github.comgithub.com。這在 Stable Diffusion 上節省了 ~32% 的 FLOP，而輸出 qualitygithub.comgithub.com 幾乎沒有損失。Delta Distillation 和 Clockwork Diffusion 都展示了 Qualcomm 的演算法級創新：它們利用時間或反覆運算冗餘來避免重複工作，使用近似（delta 或緩存特徵）來減少計算，同時保持視覺保真度。Architectural Support in ISPs and DSPs: On the hardware side, Qualcomm’s imaging and vision accelerators also incorporate precision-scaling and approximate arithmetic. The Snapdragon Adreno GPU and Hexagon DSP (often used for camera AI and image processing) support mixed precision operations. A Qualcomm patent on a “programmable streaming processor” (likely an early Adreno GPU design) explicitly describes having both full-precision and half-precision arithmetic units, where less critical calculations can be executed in half precision for power savings
. By using more 16-bit ALUs in place of 32-bit ALUs, the GPU can increase throughput or reduce energy at the cost of some numerical accuracy. This concept is now standard in mobile GPUs (e.g. FP16 rendering) and ISP cores (the Spectra ISP uses a mix of 18-bit and lower-precision processing). In Qualcomm’s Hexagon DSP, which often handles computer vision kernels at the edge, integer quantization (8-bit math) is heavily employed to accelerate inference within the power budget. Qualcomm’s documentation highlights that 8-bit quantization can cut energy per operation by 30× compared to 32-bit floats. In summary, by lowering numerical precision (through fixed-point arithmetic, half-precision floats, etc.), Qualcomm’s imaging and vision hardware leverages approximate computing principles to achieve real-time performance on battery-powered devices.ISP 和 DSP 中的架構支援： 在硬體方面，Qualcomm 的成像和視覺加速器還集成了精確縮放和近似算術。Snapdragon Adreno GPU 和 Hexagon DSP（通常用於相機 AI 和圖像處理）支援混合精度運算。Qualcomm 關於「可程式設計流處理器」的專利（可能是早期的 Adreno GPU 設計）明確描述了具有全精度和半精度算術單位，其中不太關鍵的計算可以以半精度執行，以實現功率 savingspatents.google.compatents.google.com。通過使用更多的 16 位 ALU 代替 32 位 ALU，GPU 可以提高輸送量或降低能耗，但代價是一些數值 accuracypatents.google.compatents.google.com。這個概念現在是移動 GPU（例如 FP16 渲染）和 ISP 內核（Spectra ISP 混合使用 18 位和低精度處理）的標準配置。在 Qualcomm 的 Hexagon DSP 中，通常在邊緣處理電腦視覺內核，大量使用整數量化（8 位數學）來加速功耗預算內的推理。Qualcomm 的文檔強調，與 32 位量化相比，8 位量化可以將每次作的能耗降低 30×floatsqualcomm.comqualcomm.com。總之，通過降低數值精度（通過定點算術、半精度浮點數等），Qualcomm 的成像和視覺硬體利用近似計算原理在電池供電設備上實現即時性能。Case Study – Low-Power Video: Table 1 summarizes how these approximate computing techniques manifest in Qualcomm’s camera/vision R&D:案例研究 – 低功耗視頻： 表 1 總結了這些近似計算技術在 Qualcomm 的相機/視覺研發中是如何體現的：
現代智慧手機攝像頭和設備端視覺 AI 任務為近似計算提供了理想的用例：它們在緊湊的功耗和延遲預算下處理大量感測器數據，並且輸出（圖像或檢測到的物體）只需要在感知或統計上正確，而不是精確
。圖像管道中的演算法近似：Qualcomm 的 Snapdragon 平臺包括 Spectra™ 圖像信號處理器 （ISP），它可以即時執行複雜的成像任務（降噪、HDR 混合等）。其中許多任務使用近似來提高效率。例如，最近的 Qualcomm 專利描述了視頻處理中的自適應計算跳過 – 如果幀之間的全域運動很小，ISP 可以跳過局部運動估計步驟以節省 powerpatent.nweon.com。而 ISP 則使用一種廉價的全域變換，它以更少的 computationpatent.nweon.compatent.nweon.com 近似於完全運動補償的結果。另一項關於多幀圖像融合的專利顯示 ISP 應用色調映射來「將一組顏色映射到另一組顏色......以近似高動態範圍圖像的外觀 patents.google.compatents.google.com – 本質上是一種快速的啟發式 HDR 類比，它以一些保真度換取速度。這些示例說明瞭 Qualcomm 在攝像機管道中使用演算法級近似（跳過或簡化處理）來滿足即時約束。邊緣視覺推理：用於攝像頭應用（例如視頻中的物件檢測）的設備端 AI 是 Qualcomm 使用 AxC 的另一個領域。 高通公司領導的一項著名研究是 Delta Distillation（Habibian 等人，ECCV 2022），這是一種通過近似每幀 processinggithub.comgithub.com 來加速視頻神經網路的方法。第一幀被完全處理，後續幀表示為增量（差異），通過更小的網路傳遞，而不是在每幀上運行繁重的 CNN。這個學生網路通過從教師 networkgithub.comgithub.com 中提煉知識來學習預測特徵/幀輸出相對於參考幀的變化。結果是時間相鄰幀中的冗餘資訊不會從頭開始重新計算——這是一種利用視頻冗餘的時間近似形式。這項技術由 Qualcomm AI Research 與學術界合作開發，以最低的準確率 lossgithub.comgithub.com 顯著提高了視頻任務的速度。它反映了用於視覺的神經網路近似器的更廣泛策略：使用較小或部分模型來近似較大模型在“簡單”或冗餘輸入（如連續視頻幀）上的輸出。最近的另一項工作是 Clockwork Diffusion（Qualcomm AI Research 2023），它針對生成式視覺模型。 它觀察到，並非擴散模型中的所有降噪步驟對輸出的影響都相同;通過對低解析度特徵圖重用前面步驟的計算，後面的步驟可以使用近似緩存 resultgithub.comgithub.com。這在 Stable Diffusion 上節省了 ~32% 的 FLOP，而輸出 qualitygithub.comgithub.com 幾乎沒有損失。Delta Distillation 和 Clockwork Diffusion 都展示了 Qualcomm 的演算法級創新：它們利用時間或反覆運算冗餘來避免重複工作，使用近似（delta 或緩存特徵）來減少計算，同時保持視覺保真度。ISP 和 DSP 中的架構支援：在硬體方面，Qualcomm 的成像和視覺加速器還集成了精確縮放和近似算術。Snapdragon Adreno GPU 和 Hexagon DSP（通常用於相機 AI 和圖像處理）支援混合精度運算。Qualcomm 關於「可程式設計流處理器」的專利（可能是早期的 Adreno GPU 設計）明確描述了具有全精度和半精度算術單位，其中不太關鍵的計算可以以半精度執行，以實現功率 savingspatents.google.compatents.google.com。通過使用更多的 16 位 ALU 代替 32 位 ALU，GPU 可以提高輸送量或降低能耗，但代價是一些數值 accuracypatents.google.compatents.google.com。這個概念現在是移動 GPU（例如 FP16 渲染）和 ISP 內核（Spectra ISP 混合使用 18 位和低精度處理）的標準配置。在 Qualcomm 的 Hexagon DSP 中，通常在邊緣處理電腦視覺內核，大量使用整數量化（8 位數學）來加速功耗預算內的推理。 Qualcomm 的文檔強調，與 32 位量化相比，8 位量化可以將每次作的能耗降低 30×floatsqualcomm.comqualcomm.com。總之，通過降低數值精度（通過定點算術、半精度浮點數等），Qualcomm 的成像和視覺硬體利用近似計算原理在電池供電設備上實現即時性能。案例研究 - 低功耗視頻： 表 1 總結了這些近似計算技術在高通的相機/視覺研發中是如何體現的：
| Technique & Domain | Qualcomm Innovation | Approach and Benefit | Source / Example |
|---|---|---|---|
| Motion estimation skip (Video ISP) | Qualcomm Patent (2024): “Scale image resolution for motion estimation” | Skips fine local motion computation when global motion is low, using a cheaper global model instead (trading some accuracy for power savings) | Patent pub. US20240005518 |
| Tone-mapping approximation (Image fusion) | Qualcomm Patent (2023): Multi-frame low-light imaging pipeline | Uses tone-mapping to approximate an HDR image look from standard frames (fast heuristic in place of true multi-exposure HDR) | Patent US11727540B2 |
| Delta Distillation (Video AI) | ECCV 2022 paper by Qualcomm AI Research (Habibian et al.) | Uses a small “delta” neural net on frame differences, instead of full CNN on every frame. Preserves accuracy (~95–98%) with much less computation | Qualcomm AI Blog / Code release |
| Clockwork Diffusion (Generative AI) | Qualcomm AI Research 2023 (model-step distillation) | Reuses high-cost computation for low-res features across multiple diffusion steps. Achieves ~30% compute reduction with negligible output change | ArXiv 2023 (Yang et al.) |
| Mixed-precision ISP/GPU (Image processing hardware) | Qualcomm GPU/ISP Architecture Patents (2014–2018) | Incorporate half-precision (16-bit) ALUs and registers alongside full precision; use lower precision for error-tolerant portions of image algorithms, saving power/area | US Patent 8,633,936 B2; Adreno/Spectra product briefs |
Table 1: Examples of approximate computing in Qualcomm’s handheld imaging and edge AI R&D.表 1：Qualcomm 手持式成像和邊緣 AI 研發中的近似計算範例。
In all these cases, Qualcomm’s designs leverage the inherent tolerance in human vision and high-level AI outcomes. A slightly noisier image or an occasional minor error in object detection is an acceptable trade-off if it yields substantial gains in efficiency. By co-designing algorithms (e.g. frame skipping, distillation) and hardware (e.g. mixed-precision units) around this tolerance, Qualcomm enables smartphone cameras and edge AI to achieve impressive performance under severe energy constraints.在所有這些情況下，高通的設計都利用了人類視覺和高級 AI 結果的固有容忍度。如果圖像噪點稍大或物體檢測中偶爾出現輕微錯誤，如果能顯著提高效率，則這是一種可以接受的折衷方案。通過圍繞此容差共同設計演算法（例如跳幀、蒸餾）和硬體（例如混合精度單位），Qualcomm 使智慧手機攝像頭和邊緣 AI 能夠在嚴重的能源限制下實現令人印象深刻的性能。
表 1：Qualcomm 手持式成像和邊緣 AI 研發中的近似計算示例。
在所有這些情況下，高通的設計都利用了人類視覺和高級 AI 結果的固有容忍度。如果圖像噪點稍大或物體檢測中偶爾出現輕微錯誤，如果能顯著提高效率，則這是一種可以接受的折衷方案。通過圍繞此容差共同設計演算法（例如跳幀、蒸餾）和硬體（例如混合精度單位），Qualcomm 使智慧手機攝像頭和邊緣 AI 能夠在嚴重的能源限制下實現令人印象深刻的性能。Approximate Computing in AI Accelerators (Hexagon DSP & AI Engine)
Beyond imaging, Qualcomm has invested heavily in general-purpose AI acceleration on devices – notably through the Hexagon DSP/AI Engine in Snapdragon SoCs. Approximate computing techniques are at the core of making on-device AI feasible.除了成像之外，高通還在設備上的通用 AI 加速方面進行了大量投資，特別是通過 Snapdragon SoC 中的 Hexagon DSP/AI 引擎。近似計算技術是使設備端 AI 可行的核心。
Quantization and Precision Scaling: Qualcomm’s AI Engine relies extensively on precision scaling to run neural networks efficiently. Quantization – reducing weights and activations from 32-bit or 16-bit to 8-bit or lower – is a prime example of approximate computing in AI. Qualcomm AI Research has been a leader in quantization algorithms. A notable paper is “Relaxed Quantization for Discretized Neural Networks” (Louizos et al., ICLR 2019), co-authored by Qualcomm researchers
. This work introduced a method to smooth out the quantization process during training (treating quantization levels as learnable, continuous parameters that gradually become discrete). The result was higher accuracy at low bit-widths – effectively pushing the frontier of how far one can approximate a 32-bit network with an 8-bit one. Qualcomm swiftly brought this research into practice: their AI Model Efficiency Toolkit (AIMET) incorporated techniques like relaxed quantization to help developers compress models for Snapdragon devices. By 2023, Qualcomm was granted a patent titled “Continuous relaxation of quantization for discretized deep neural networks,” corresponding to this approach. This synergy of publication and patent shows a clear research-to-product pipeline, where advanced approximation algorithms devised by Qualcomm AI Research directly enhance on-chip inference.量化和精確縮放：Qualcomm 的 AI 引擎廣泛依賴精確擴展來高效運行神經網路。量化 – 將權重和啟動從 32 位或 16 位減少到 8 位或更低 – 是 AI 中近似計算的一個典型示例。Qualcomm AI Research 一直是量化演算法的領導者。一篇著名的論文是 Qualcomm researchersqualcomm.com 合著的 “離散神經網络的鬆弛量化”（Louizos et al.， ICLR 2019）。這項工作介紹了一種在訓練過程中平滑量化過程的方法（將量化水平視為可學習的、逐漸變得離散的連續參數）。結果是在低位寬下具有更高的精度 - 有效地推動了 32 位網路與 8 位 onequalcomm.com 的近似程度的前沿。Qualcomm 迅速將這項研究付諸實踐：他們的 AI 模型效率工具包 （AIMET） 結合了鬆散量化等技術，以幫助開發人員為 Snapdragon devicesqualcomm.com 壓縮模型。到 2023 年，高通公司獲得了一項與此 approachpatents.google.com 相對應的專利，標題為 「離散深度神經網路的量化的連續放寬 」。這種 publicationqualcomm.com 和專利的協同作用顯示了一個清晰的研究到產品管道，其中 Qualcomm AI Research 設計的高級近似演算法直接增強了片上推理。In hardware, Qualcomm’s Hexagon NPU (Neural Processing Unit) and Hexagon Vector eXtensions (HVX) are designed for INT8 arithmetic as the sweet spot for power and accuracy. An internal Qualcomm presentation notes that moving from 32-bit float to 8-bit integer yields ~30× reduction in energy per addition and ~18× for multiplications
, plus significant memory and latency savings. These huge gains come at the cost of minimal accuracy loss in many networks – a classic approximate computing trade-off that Qualcomm exploits. Furthermore, Qualcomm has filed patents to extend this quantization paradigm at the instruction-set level. For example, US Patent 12,159,140 (granted Dec. 2024) describes a specialized vector instruction for neural network quantization. A single instruction can take a block of data (say 16-bit or 32-bit values) and quantize them down to lower-bit precision (e.g. 8-bit), performing scaling and zero-point offset and packing the results in one go. By embedding this into the ISA, the hardware accelerates the approximate computing step (quantization) directly. This co-design reduces the overhead of quantization and makes dynamic precision adjustment viable even at runtime. It illustrates Qualcomm’s holistic approach: not just using low precision, but innovating new hardware instructions to perform the approximation efficiently.在硬體方面，Qualcomm 的 Hexagon NPU（神經處理單元）和 Hexagon Vector eXtensions （HVX） 專為 INT8 演算法而設計，作為功率和準確性的最佳點。Qualcomm 內部演示指出，從 32 位浮點數遷移到 8 位整數，每次添加的能量減少 ~30×，multiplicationsqualcomm.comqualcomm.com 能耗降低 ~18×，並顯著節省記憶體和延遲。這些巨大的收益是以許多網路中的最小精度損失為代價的——這是高通公司利用的經典近似計算權衡。此外，Qualcomm 已經申請了專利，以在指令集級別擴展這種量化範式。例如，美國專利 12,159,140（2024 年 12 月授予）描述了用於神經網路量化 patents.justia.compatents.justia.com 的專用向量指令 。單個指令可以獲取一個數據塊（例如 16 位或 32 位值）並將它們量化為較低的位精度（例如 8 位），執行縮放和零點偏移並將結果打包在一個 gopatents.justia.compatents.justia.com 中。通過將其嵌入到 ISA 中，硬體可以直接加速近似計算步驟（量化）。這種協同設計減少了量化的開銷，即使在運行時也可以進行動態精度調整。它說明瞭 Qualcomm 的整體方法：不僅使用低精度，而且創新新的硬體指令來高效執行近似。Network Pruning and Compression: Besides quantization, Qualcomm’s R&D covers other model compression techniques (pruning, low-rank approximations, etc.), which are essentially forms of approximate computing (removing less-important weights/neurons to streamline a network). A series of Qualcomm patents in 2018–2024 addresses this. For instance, “Lossy layer compression for dynamic scaling of DNN processing” (Qualcomm, granted Apr. 2024) proposes selectively compressing or skipping neural network layers during inference to save computation
. If certain layers can be approximated (e.g. via a cheaper tensor factorization or by gating them off under certain conditions), the model can dynamically trade off accuracy for efficiency. Another patent, “Neural network compression via weak supervision” (Qualcomm, 2018) suggests training a smaller “student” network under the guidance of a large one (similar to knowledge distillation). These ideas echo academic themes of neural approximators – simpler networks approximating more complex ones – which Qualcomm has both published and patented. While specific product implementations are under wraps, it’s likely that Qualcomm’s AI runtime (the Qualcomm Neural Processing SDK and AIMET toolkit) includes support for using such compressed or partially executed models on Hexagon. The end effect is that the SoC can scale AI workload quality to meet power budgets: e.g., running a smaller model when battery is low or when thermal headroom is limited, an embodiment of approximate computing at the system level.網路修剪和壓縮： 除了量化，高通的研發還涵蓋了其他模型壓縮技術（修剪、低秩近似等），這些技術本質上是近似計算的形式（刪除不太重要的權重/神經元以簡化網路）。2018-2024 年的一系列高通專利解決了這個問題。例如，「用於 DNN 處理動態擴展的有損層壓縮」（Qualcomm，2024 年 4 月授予）建議在推理過程中選擇性壓縮或跳過神經網路層以節省 computationpatents.google.com。如果某些層可以近似化（例如，通過更便宜的張量分解或在某些條件下將它們關閉），則模型可以動態地權衡準確性以換取 efficiencypatents.google.com。另一項專利 “通過弱監督進行神經網络壓縮”（Qualcomm，2018 年）建議在大型網络（類似於知識蒸餾）的指導下訓練較小的“學生”網络 patents.google.com。這些想法與高通公司已經發佈的神經逼近器的學術主題相呼應——更簡單的網路逼近更複雜的網路——並獲得了專利。雖然具體的產品實現處於保密狀態，但 Qualcomm 的 AI 運行時（Qualcomm 神經處理 SDK 和 AIMET 工具包）很可能支援在 Hexagon 上使用此類壓縮或部分執行的模型。最終效果是 SoC 可以擴展 AI 工作負載品質以滿足功率預算：例如，在電池電量不足或熱餘量有限時運行較小的模型，這是系統級近似計算的體現。Hardware Error Resilience: At the circuit level, Qualcomm’s AI accelerators also deal with approximation by tolerating and correcting errors due to aggressive voltage-frequency scaling. Qualcomm designers have spoken about “error-tolerant design” and “adaptive voltage scaling with error feedback” in mobile processors
. In practice, this means the AI cores might be run at lower voltages than normally required, accepting an occasional timing error, which can be detected and corrected by micro-architectural features (such as Razor flip-flops or instruction replay). By tolerating rare errors, the average power is reduced significantly – a technique used in some commercial CPUs and likely in Qualcomm’s Kryo CPUs and Hexagon DSPs as well. While Qualcomm hasn’t publicly detailed its error-checking circuits, a 2014 Qualcomm presentation listed Error Tolerant Design, Clock Stretching, and DVFS together as key low-power techniques, implying the use of hardware that can detect timing errors and stretch clock cycles or adjust voltage on the fly to prevent failure. This approach, fundamentally, is approximate computing at the electrical level: the circuit is allowed to operate in an unsafe regime (approximating correct timing) with mechanisms to catch the resulting errors. The benefit is a reduction of worst-case voltage margin, saving power when typical operations don’t need the full safety guardband.硬體錯誤恢復能力： 在電路級，Qualcomm 的 AI 加速器還通過容忍和糾正由於激進的電壓-頻率縮放而導致的誤差來處理近似。Qualcomm 設計人員在移動 processorsislped.org 中談到了 “容錯設計” 和 “帶誤差反饋的自適應電壓縮放”。 在實踐中，這意味著 AI 內核可能會以低於正常要求的電壓運行，接受偶爾的時序錯誤，這可以通過微架構功能（例如 Razor 觸發器或指令重放）來檢測和糾正。通過容忍罕見的錯誤，平均功耗顯著降低——這是一些商用 CPU 中使用的技術，也可能用於高通的 Kryo CPU 和 Hexagon DSP。雖然高通尚未公開詳細說明其錯誤檢查電路，但 2014 年高通的一次演示將容錯設計、時鐘擴展和 DVFS 列為關鍵的低功耗 techniquesislped.org，這意味著使用可以檢測時序錯誤和拉伸時鐘週期或動態調整電壓以防止故障的硬體。從根本上說，這種方法是電氣級別的近似計算： 允許電路在不安全的狀態下運行（近似正確的時序），並有機制來捕獲產生的錯誤。其優點是降低了最壞情況下的電壓裕量，在典型作不需要完整的安全防護帶時節省了功耗。In summary, Qualcomm’s AI accelerators demonstrate approximate computing through: (a) model approximations (quantization, pruning, etc.) to reduce required compute, (b) custom hardware ISA and units to perform low-precision operations efficiently, and (c) resilient circuit designs that squeeze power by operating near the edge of failure but correcting errors. These innovations have been crucial in delivering Qualcomm’s on-device AI performance. The table below highlights a few representative patents and products in this category:總之，Qualcomm 的 AI 加速器通過以下方式演示近似計算：（a） 模型近似（量化、修剪等）以減少所需的計算，（b） 自定義硬體 ISA 和單元以高效執行低精度作，以及 （c） 彈性電路設計，通過在故障邊緣附近運行但糾正錯誤來壓縮功率。這些創新對於提供 Qualcomm 的設備端 AI 性能至關重要。下表重點介紹了該類別中的一些代表性專利和產品：
除了成像之外，高通還在設備上的通用 AI 加速方面進行了大量投資，特別是通過 Snapdragon SoC 中的 Hexagon DSP/AI 引擎。近似計算技術是使設備端 AI 可行的核心。
量化和精確縮放：Qualcomm 的 AI 引擎廣泛依賴精確縮放來高效運行神經網路。量化 – 將權重和啟動從 32 位或 16 位減少到 8 位或更低 – 是 AI 中近似計算的一個典型示例。Qualcomm AI Research 一直是量化演算法的領導者。一篇著名的論文是 Qualcomm researchersqualcomm.com 合著的“離散神經網络的鬆弛量化”（Louizos et al.， ICLR 2019）。這項工作介紹了一種在訓練過程中平滑量化過程的方法（將量化水平視為可學習的、逐漸變得離散的連續參數）。 結果是在低位寬下具有更高的精度 - 有效地推動了 32 位網路與 8 位 onequalcomm.com 的近似程度的前沿。Qualcomm 迅速將這項研究付諸實踐：他們的 AI 模型效率工具包 （AIMET） 結合了鬆散量化等技術，以幫助開發人員為 Snapdragon devicesqualcomm.com 壓縮模型。到 2023 年，高通獲得了一項名為「離散深度神經網路的量化的連續放寬」的專利，與此 approachpatents.google.com 相對應的。這種 publicationqualcomm.com 和專利的協同作用顯示了一個清晰的研究到產品管道，其中 Qualcomm AI Research 設計的高級近似演算法直接增強了片上推理。在硬體方面，Qualcomm 的 Hexagon NPU（神經處理單元）和 Hexagon Vector eXtensions （HVX） 專為 INT8 演算法而設計，作為功率和準確性的最佳點。Qualcomm 內部演示指出，從 32 位浮點數遷移到 8 位整數，每次添加的能量減少 ~30×，multiplicationsqualcomm.comqualcomm.com 能耗降低 ~18×，並顯著節省記憶體和延遲。這些巨大的收益是以許多網路中的最小精度損失為代價的——這是高通公司利用的經典近似計算權衡。此外，Qualcomm 已經申請了專利，以在指令集級別擴展這種量化範式。例如，美國專利 12,159,140（12 月授予。 2024 年）描述了神經網路 quantizationpatents.justia.compatents.justia.com 的專用向量指令。單個指令可以獲取一個數據塊（例如 16 位或 32 位值）並將它們量化為較低的位精度（例如 8 位），執行縮放和零點偏移並將結果打包在一個 gopatents.justia.compatents.justia.com 中。通過將其嵌入到 ISA 中，硬體可以直接加速近似計算步驟（量化）。這種協同設計減少了量化的開銷，即使在運行時也可以進行動態精度調整。它說明瞭 Qualcomm 的整體方法：不僅使用低精度，而且創新新的硬體指令來高效執行近似。網路修剪和壓縮：除了量化，Qualcomm 的研發還涵蓋了其他模型壓縮技術（修剪、低秩近似等），這些技術基本上是近似計算的形式（去除不太重要的權重/神經元以簡化網路）。2018-2024 年的一系列高通專利解決了這個問題。例如，「用於動態擴展 DNN 處理的有損層壓縮」（Qualcomm，授予 Apr. 2024） 提議在推理過程中選擇性地壓縮或跳過神經網路層以節省 computationpatents.google.com。如果某些層可以近似化（例如，通過更便宜的張量分解或在某些條件下將它們關閉），則模型可以動態地權衡準確性以換取 efficiencypatents.google.com。另一項專利“通過弱監督進行神經網络壓縮”（Qualcomm，2018 年）建議在大型網络（類似於知識蒸餾）的指導下訓練較小的“學生”網络 patents.google.com。這些想法與高通公司已經發佈的神經逼近器的學術主題相呼應——更簡單的網路逼近更複雜的網路——並獲得了專利。雖然具體的產品實現處於保密狀態，但 Qualcomm 的 AI 運行時（Qualcomm 神經處理 SDK 和 AIMET 工具包）很可能支援在 Hexagon 上使用此類壓縮或部分執行的模型。最終效果是 SoC 可以擴展 AI 工作負載品質以滿足功率預算：例如，在電池電量不足或熱餘量有限時運行較小的模型，這是系統級近似計算的體現。硬體錯誤彈性：在電路級別，Qualcomm 的 AI 加速器還通過容忍和糾正由於激進的電壓-頻率縮放而導致的錯誤來處理近似。Qualcomm 設計人員在移動 processorsislped.org 中談到了“容錯設計”和“帶誤差反饋的自適應電壓縮放”。在實踐中，這意味著 AI 內核可能會以低於正常要求的電壓運行，接受偶爾的時序錯誤，這可以通過微架構功能（例如 Razor 觸發器或指令重放）來檢測和糾正。 通過容忍罕見的錯誤，平均功耗顯著降低——這是一些商用 CPU 中使用的技術，也可能用於高通的 Kryo CPU 和 Hexagon DSP。雖然高通尚未公開詳細說明其錯誤檢查電路，但 2014 年高通的一次演示將容錯設計、時鐘擴展和 DVFS 列為關鍵的低功耗 techniquesislped.org，這意味著使用可以檢測時序錯誤和拉伸時鐘週期或動態調整電壓以防止故障的硬體。從根本上說，這種方法是電氣級別的近似計算： 允許電路在不安全的狀態下運行（近似正確的時序），並有機制來捕獲產生的錯誤。其優點是降低了最壞情況下的電壓裕量，在典型作不需要完整的安全防護帶時節省了功耗。總之，Qualcomm 的 AI 加速器通過以下方式演示近似計算：（a） 模型近似（量化、修剪等）以減少所需的計算，（b） 自定義硬體 ISA 和單元以高效執行低精度作，以及 （c） 彈性電路設計，通過在故障邊緣附近運行但糾正錯誤來壓縮功率。這些創新對於提供 Qualcomm 的設備端 AI 性能至關重要。下表重點介紹了該類別中的一些代表性專利和產品：| Approximation Technique | Qualcomm Patent / Publication (Year) | Key Idea and Application | Integration in Products |
|---|---|---|---|
| Relaxed Quantization (8-bit) | Louizos et al., ICLR 2019; Patent US11562208B2 (2023) | Continuous relaxation during training to allow ultra-low precision without accuracy loss. Quantize neural network weights/activations to 4–8 bits effectively. | Snapdragon AI Model Efficiency Toolkit (AIMET) implements this for Hexagon (enabling INT8 inference in AI Engine) |
| Quantization ISA Extension | Patent US12159140 (Qualcomm, Dec 2024) | A single vector instruction performs M→P bit quantization (with scaling and packing) on-the-fly. Speeds up quantization and data packing for neural ops. | Hexagon DSP (latest generations) – e.g. support for VNNI-style dot products and packing for INT8, improving efficiency of quantized NN inference. |
| Neural Network Pruning/Skipping | Patent US11961000B2 (Qualcomm, 2024) – Lossy layer compression | Dynamically drop or compress certain DNN layers when possible (depending on input or context), to save compute at expense of some accuracy. | Integrated in runtime as “adaptive AI execution.” (E.g., Snapdragon can run a lighter sub-model when full accuracy not needed, though not marketed explicitly.) |
| Knowledge Distillation | Patent US20180260695A1 (Qualcomm, 2018) – Weakly-supervised NN compression | Train smaller networks under supervision of large ones, preserving behavior with fewer resources – an approximate replica of a heavy model. | Qualcomm AI Research “student-teacher” techniques; likely used internally for creating efficient models (e.g., MobileNet variants for Snapdragon). |
| Timing Error Tolerance | (Design technique in Qualcomm SoCs, ~2014) | Use error-detecting sequential circuits and clock stretching to allow lower voltages. Occasional timing errors are caught and corrected, letting average operation be closer to failure threshold (approximate timing). | Kryo CPU and possibly Hexagon cores employ adaptive voltage scaling (AVS) and dynamic clock adjustment. This reduces power consumption in AI workloads on Snapdragon CPUs/DSPs, with slight performance variance. |
Table 2: Qualcomm’s approximate computing strategies in AI accelerators (publications and patents).表 2：Qualcomm 在 AI 加速器中的近似計算策略（出版物和專利）。
Qualcomm’s commercial Snapdragon platforms highlight these capabilities under marketing terms like “Qualcomm AI Engine” – which encompasses the Hexagon Tensor Accelerator, HVX, and the AI software stack. The AI Engine explicitly supports integer 8-bit inference and mixed precision, a direct outcome of the R&D discussed. In addition, Qualcomm often cites how their research “drives the industry towards integer inference and power-efficient AI”
– a nod to the fact that approximate computing (via quantization/compression) is the enabler of running advanced AI on battery-powered devices.Qualcomm 的商業 Snapdragon 平臺在 “Qualcomm AI Engine” 等行銷術語下突出了這些功能，其中包括 Hexagon Tensor Accelerator、HVX 和 AI 軟體堆棧。AI 引擎明確支援整數 8 位推理和混合精度，這是 R&D 討論的直接結果。此外，高通公司經常引用他們的研究如何「推動行業朝著整數推理和節能 AI 發展 qualcomm.com——這是對近似計算（通過量化/壓縮）是在電池供電設備上運行高級 AI 的推動因素這一事實的認可。表 2：Qualcomm 在 AI 加速器中的近似計算策略（出版物和專利）。
Qualcomm 的商業 Snapdragon 平臺在“Qualcomm AI Engine”等行銷術語下突出了這些功能，其中包括 Hexagon Tensor Accelerator、HVX 和 AI 軟體堆棧。AI 引擎明確支援整數 8 位推理和混合精度，這是 R&D 討論的直接結果。此外，高通公司經常引用他們的研究如何「推動行業朝著整數推理和節能 AI 發展 qualcomm.com——這是對近似計算（通過量化/壓縮）是在電池供電設備上運行高級 AI 的推動因素這一事實的認可。Approximate Techniques in Low-Power Embedded & IoT Systems
Qualcomm’s portfolio also extends to IoT, wearables, and MCU-class chips (for example, the Qualcomm 212 LTE IoT modem, Qualcomm QCC series for Bluetooth audio, etc.). In these ultra-low-power domains, approximate computing is equally, if not more, critical due to tiny energy budgets. While less information is publicly available (since these designs are often proprietary), we can infer and highlight relevant R&D activities:Qualcomm 的產品群組還擴展到物聯網、穿戴式裝置和 MCU 級晶片（例如，Qualcomm 212 LTE 物聯網數據機、用於藍牙音訊的高通 QCC 系列等）。在這些超低功耗域中，由於能量預算很小，近似計算同樣重要，甚至更重要。雖然公開可用的資訊較少（因為這些設計通常是專有的），但我們可以推斷並強調相關的研發活動：
Voltage Overscaling and Inexact Circuits: IoT devices often operate on the edge of minimal power supply to prolong battery life (or even run on energy harvesting). Qualcomm’s designs for IoT likely use aggressive DVFS and even sub-threshold operation where possible, accepting that computation may occasionally be faulty. A survey co-authored by a Qualcomm researcher notes that Voltage Over-Scaling (VOS) has “already been applied to several smart sensing systems”
. This means lowering voltage below the safe level, producing stochastic bit errors in logic operations, which are then either corrected or deemed tolerable for the application. For example, a low-power Qualcomm GPS (GNSS) chip might use reduced precision or occasional errors in correlation computations if it still yields an acceptable fix for location. Indeed, common DSP algorithms in sensing/positioning can often endure reduced sampling rates, inexact arithmetic, and algorithmic alterations without breaking functionality. Qualcomm’s research involvement in projects like APROPOS (Approximate Computing for Power and Energy Optimization) further underscores industry-wide efforts to apply AxC techniques in IoT devices.電壓過標和不精確電路：IoT 設備通常在最小電源的邊緣運行，以延長電池壽命（甚至依靠能量收集運行）。Qualcomm 的 IoT 設計可能會盡可能使用激進的 DVFS 甚至亞閾值作，並接受計算偶爾可能會出錯。高通研究人員合著的一項調查指出， 電壓過縮放 （VOS） “已經應用於多個智慧感測系統”pure.qub.ac.ukpure.qub.ac.uk。這意味著將電壓降低到安全水準以下，在 logic 作中產生隨機位錯誤，然後這些錯誤被糾正或被認為可以容忍。例如，如果低功耗 Qualcomm GPS （GNSS） 晶片仍能產生可接受的位置修復，則它可能會在相關計算中使用降低的精度或偶爾出現的錯誤。事實上，感測/定位中的常見 DSP 演算法通常可以承受降低的採樣率、不精確的算術和演算法更改 ，而不會破壞 functionalitypure.qub.ac.ukpure.qub.ac.uk。Qualcomm 參與 APROPOS （Approximate Computing for Power and Energy Optimization） 等專案的研究進一步強調了整個行業在物聯網 devicesieeexplore.ieee.org 中應用 AxC 技術的努力。Approximate Memory and Storage: Memory operations can dominate energy use in IoT SoCs. Qualcomm has explored making memory more approximate to save power. One approach is unequal error protection – storing critical data (e.g., program code or important parameters) in reliable memory cells, but storing error-tolerant data (e.g. sensor readings, or neural network weights) in low-voltage or smaller cells that may fail more often. Academic research sponsored by Qualcomm has proposed hybrid memory architectures where a portion of an on-chip SRAM uses fewer transistors (6T cells) to save area/power at the cost of stability, suitable for “good enough” storage
. This approximate SRAM could be used for caching image or audio data in wearables. While specific Qualcomm patents on approximate memory have not surfaced, their low-power IoT devices likely use aggressive memory compression and lossy data storage. For instance, the Qualcomm 212 LTE IoT modem (an NB-IoT chipset) advertises micro-amp level sleep currents – which implies extreme duty cycling and data approximation (the device might drop precision or compress sensor data before storing it to RAM, to spend less time awake).近似記憶體和存儲： 記憶體作可能主導 IoT SoC 的能源使用。Qualcomm 已經探索使記憶體更接近以節省功耗。一種方法是不等錯誤保護 – 將關鍵數據（例如，程式代碼或重要參數）存儲在可靠的存儲單元中，但將容錯數據（例如感測器讀數或神經網路權重）存儲在低電壓或更小的單元中，這些單元可能會更頻繁地發生故障。由 Qualcomm 贊助的學術研究提出了混合記憶體架構，其中片上 SRAM 的一部分使用更少的晶體管（6T 單元），以犧牲穩定性為代價來節省面積/功耗，適用於“足夠好”的 storagescholar.google.co.in。這種近似的 SRAM 可用於在穿戴式裝置中快取影像或音訊數據。雖然 Qualcomm 在近似記憶體方面的具體專利尚未浮出水面，但其低功耗 IoT 設備可能使用激進的記憶體壓縮和有損數據存儲。例如，Qualcomm 212 LTE 物聯網數據機（一種 NB-IoT 晶片組）qualcomm.com 公佈微安級休眠電流 ——這意味著極端的佔空比和數據近似（設備可能會在將感測器數據儲存到 RAM 之前降低精度或壓縮感測器數據，以減少喚醒時間）。Lightweight AI with Approximations: IoT and wearable chips are increasingly running AI workloads (e.g., keyword spotting on a smartwatch, activity classification on a fitness band). Qualcomm’s AI research in hyper-dimensional computing and tinyML intersects with approximate computing – using simplified models and binary/ternary neural networks for example. Qualcomm has acquired companies (like EdgeQ and the recent investment in Edge Impulse) to bolster on-device AI for IoT
. The models running on these micro-class chips use heavy quantization (often 8-bit or lower, even analog computing in some cases) and approximated neural architectures to fit within strict memory and energy limits. A Qualcomm engineer’s LinkedIn mention of “Approximate Computing for prevention of Side-Channel Attacks” hints that approximate computing in IoT isn’t just for efficiency but can also add noise that improves security/privacy – a fascinating dual benefit.具有近似值的輕量級 AI： 物聯網和可穿戴晶元越來越多地運行 AI 工作負載（例如，智慧手錶上的關鍵字識別、健身手環上的活動分類）。Qualcomm 在超維計算和 tinyML 方面的 AI 研究與近似計算相交——例如，使用簡化模型和二進位/三元神經網路。高通已經收購了多家公司（如 EdgeQ 和最近對 Edge Impulse 的投資），以支援 IoTiotworldtoday.comiotworldtoday.com 設備端 AI。在這些微類晶元上運行的模型使用大量量化（通常為 8 位或更低，在某些情況下甚至使用類比計算）和近似神經架構 ，以適應嚴格的記憶體和能量限制。高通工程師 LinkedIn 提到 「用於防止側通道攻擊的近似計算」qualcomm.com 這暗示了物聯網中的近似計算不僅是為了提高效率，而且還會增加雜訊，從而提高安全性/隱私性——這是一個迷人的雙重好處。Cross-layer Co-Design: A theme in Qualcomm’s IoT R&D is co-designing hardware and software to gracefully degrade quality for power savings. For example, consider a Bluetooth audio SoC in wireless earbuds: Qualcomm’s QCC512x series chips can extend battery life by lowering audio quality when the user is in a noisy environment where subtle details aren’t perceivable. This might involve dropping the audio sample precision or using an approximate noise cancellation algorithm that is less compute-heavy. The system controller can decide, based on context, how to trade off fidelity vs. battery – essentially an application of approximate computing at the system level. Qualcomm holds patents on “dynamic profile optimization” which likely cover switching to lower-power modes that are more approximate when full accuracy isn’t needed
. In the automotive IoT space, Qualcomm’s R&D on C-V2X (vehicle communication) and sensor fusion for ADAS may utilize approximations to meet real-time deadlines, using e.g. approximate filters for radar/lidar data that run faster but with slightly higher error.跨層協同設計： 高通的物聯網研發的一個主題是共同設計硬體和軟體 ，以優雅地降低品質以節省電力。例如，考慮無線耳塞中的藍牙音訊 SoC：當使用者處於無法感知細微細節的嘈雜環境中時，Qualcomm 的 QCC512x 系列晶片可以通過降低音訊質量來延長電池壽命。這可能涉及降低音頻採樣精度或使用計算量較小的近似雜訊消除演算法。系統控制器可以根據上下文決定如何在保真度與電池之間進行權衡 - 本質上是系統級近似計算的應用。Qualcomm 擁有 「dynamic profile optimization」 專利，其中可能涵蓋切換到更低功耗的模式，這些模式在無法 neededpatents.justia.compatents.justia.com 完全精度時更接近。在汽車物聯網領域，高通對 C-V2X（車輛通信）和 ADAS 感測器融合的研發可以利用近似值來滿足即時截止日期，例如使用雷達/鐳射雷達數據的近似濾波器，這些濾波器運行得更快，但誤差略高。While specifics on IoT are less published, Qualcomm’s general strategy is evident: identify what can be approximated at minimal impact to the user, and aggressively optimize it. Whether it’s lowering sensor update rates, using approximate adders in a microcontroller ALU, or compressing data transmissions (trading accuracy for lower bandwidth), Qualcomm’s IoT solutions embrace AxC to achieve ultra-low power operation. As one survey concluded, many such AxC techniques “can help improve energy efficiency in positioning and sensing applications” if applied judiciously
. Qualcomm’s role in these domains has been to bring those judicious approximations from theory into real-world products that benefit from longer battery life and higher integration.雖然 IoT 的具體細節較少公佈，但 Qualcomm 的總體策略是顯而易見的： 確定可以以對使用者最小影響進行近似的結果，並積極優化它。 無論是降低感測器更新速率、在微控制器 ALU 中使用近似加法器，還是壓縮數據傳輸（以精度換取更低的頻寬），Qualcomm 的物聯網解決方案都採用 AxC 來實現超低功耗運行。正如一項調查得出的結論，如果 judiciouslypure.qub.ac.uk 應用，許多此類 AxC 技術 “可以説明提高定位和感測應用的能源效率”。Qualcomm 在這些領域的作用是將理論中那些明智的近似值引入實際產品中，這些產品受益於更長的電池壽命和更高的集成度。Qualcomm 的產品群組還擴展到物聯網、穿戴式裝置和 MCU 級晶片（例如，Qualcomm 212 LTE 物聯網數據機、用於藍牙音訊的高通 QCC 系列等）。在這些超低功耗域中，由於能量預算很小，近似計算同樣重要，甚至更重要。雖然公開可用的資訊較少（因為這些設計通常是專有的），但我們可以推斷並強調相關的研發活動：
電壓過大和電路不精確：物聯網設備通常在最小電源供應的邊緣運行，以延長電池壽命（甚至依靠能量收集運行）。Qualcomm 的 IoT 設計可能會盡可能使用激進的 DVFS 甚至亞閾值作，並接受計算偶爾可能會出錯。高通研究人員合著的一項調查指出，電壓過縮放 （VOS） “已經應用於多個智慧感測系統”pure.qub.ac.ukpure.qub.ac.uk。這意味著將電壓降低到安全水準以下，在 logic 作中產生隨機位錯誤，然後這些錯誤被糾正或被認為可以容忍。例如，如果低功耗 Qualcomm GPS （GNSS） 晶片仍能產生可接受的位置修復，則它可能會在相關計算中使用降低的精度或偶爾出現的錯誤。事實上，感測/定位中的常見 DSP 演算法通常可以承受降低的採樣率、不精確的算術和演算法更改，而不會破壞 functionalitypure.qub.ac.ukpure.qub.ac.uk。Qualcomm 對 APPOS（用於功率和能源優化的近似計算）等專案的研究參與進一步強調了整個行業在物聯網設備中應用 AxC 技術的努力 ieeexplore.ieee.org.近似記憶體和存儲：記憶體作可能主導物聯網 SoC 的能源使用。 Qualcomm 已經探索使記憶體更接近以節省功耗。一種方法是不等錯誤保護 – 將關鍵數據（例如，程式代碼或重要參數）存儲在可靠的存儲單元中，但將容錯數據（例如感測器讀數或神經網路權重）存儲在低電壓或更小的單元中，這些單元可能會更頻繁地發生故障。由 Qualcomm 贊助的學術研究提出了混合記憶體架構，其中片上 SRAM 的一部分使用更少的晶體管（6T 單元），以犧牲穩定性為代價來節省面積/功耗，適用於“足夠好”的 storagescholar.google.co.in。這種近似的 SRAM 可用於在穿戴式裝置中快取影像或音訊數據。雖然 Qualcomm 在近似記憶體方面的具體專利尚未浮出水面，但其低功耗 IoT 設備可能使用激進的記憶體壓縮和有損數據存儲。例如，Qualcomm 212 LTE 物聯網數據機（一種 NB-IoT 晶片組）宣傳微安級睡眠 currentsqualcomm.com——這意味著極端的占空比和數據近似（設備可能會在將感測器數據存儲到 RAM 之前降低精度或壓縮感測器數據，以減少喚醒時間）。具有近似值的輕量級 AI：IoT 和可穿戴晶片越來越多地運行 AI 工作負載（例如，智慧手錶上的關鍵字識別、健身手環上的活動分類）。Qualcomm 在超維計算和 tinyML 方面的 AI 研究與近似計算相交——例如，使用簡化模型和二進位/三元神經網路。 高通已經收購了多家公司（如 EdgeQ 和最近對 Edge Impulse 的投資），以支援 IoTiotworldtoday.comiotworldtoday.com 的設備端 AI。在這些微類晶元上運行的模型使用大量量化（通常為 8 位或更低，在某些情況下甚至使用類比計算）和近似神經架構，以適應嚴格的記憶體和能量限制。高通工程師 LinkedIn 提到了「用於防止側通道攻擊的近似計算」qualcomm.com 這暗示了物聯網中的近似計算不僅是為了提高效率，而且還會增加雜訊，從而提高安全性/隱私性——這是一個迷人的雙重好處。跨層協同設計：高通的物聯網研發中的一個主題是協同設計硬體和軟體，以優雅地降低品質以節省電力。例如，考慮無線耳塞中的藍牙音訊 SoC：當使用者處於無法感知細微細節的嘈雜環境中時，Qualcomm 的 QCC512x 系列晶片可以通過降低音訊質量來延長電池壽命。這可能涉及降低音頻採樣精度或使用計算量較小的近似雜訊消除演算法。系統控制器可以根據上下文決定如何在保真度與電池之間進行權衡 - 本質上是系統級近似計算的應用。 Qualcomm 擁有 「dynamic profile optimization」 專利，這可能涵蓋切換到更低功耗的模式，這些模式在無法 neededpatents.justia.compatents.justia.com 完全精度時更接近。在汽車物聯網領域，高通對 C-V2X（車輛通信）和 ADAS 感測器融合的研發可以利用近似值來滿足即時截止日期，例如使用雷達/鐳射雷達數據的近似濾波器，這些濾波器運行得更快，但誤差略高。雖然 IoT 的具體細節較少公佈，但 Qualcomm 的總體策略是顯而易見的：確定可以以對使用者最小影響進行近似的結果，並積極優化它。無論是降低感測器更新速率、在微控制器 ALU 中使用近似加法器，還是壓縮數據傳輸（以精度換取更低的頻寬），Qualcomm 的物聯網解決方案都採用 AxC 來實現超低功耗運行。正如一項調查得出的結論，如果 judiciouslypure.qub.ac.uk 應用，許多此類 AxC 技術“可以説明提高定位和感測應用的能源效率”。Qualcomm 在這些領域的作用是將理論中那些明智的近似值引入實際產品中，這些產品受益於更長的電池壽命和更高的集成度。Hardware-Software Co-Design and Patent-Product Pipeline軟硬體協同設計和專利產品管線
Qualcomm’s work exemplifies co-design: algorithms are adjusted to hardware capabilities and vice-versa to maximize the gains from approximate computing. Many of the techniques discussed transitioned from Qualcomm’s research papers into patents and then into shipping products in a span of just a few years. Below, we draw connections between select research publications, the patented inventions, and their manifestation in Snapdragon platforms:Qualcomm 的工作是協同設計的典範：演算法根據硬體功能進行調整，反之亦然，以最大限度地從近似計算中獲得收益。討論的許多技術在短短幾年內從 Qualcomm 的研究論文轉變為專利，然後轉化為產品運輸。下面，我們總結了精選的研究出版物、專利發明及其在 Snapdragon 平臺中的表現形式之間的聯繫：
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Bayesian & Quantization Research → AI Engine: Qualcomm AI Research (with academics) published cutting-edge techniques like Bayesian compression and relaxed quantization
. These were patented and rapidly integrated into tools (AIMET) and hardware support. Qualcomm’s Hexagon DSPs started offering INT8 tensor ops and even INT4 in latest chips, showing a direct line from research on ultra-low precision to product. A Qualcomm blog in 2018 highlighted Bayesian Deep Learning’s benefit as “compression and quantization, which reduces complexity of the neural network model”, directly tying those algorithms to power-efficient AI on Snapdragon.AI 引擎→貝葉斯和量化研究：Qualcomm AI Research（與學術界合作）發表了貝葉斯壓縮和鬆弛 quantizationqualcomm.com 等尖端技術。這些已獲得專利，並迅速集成到工具 （AIMET） 和硬體支援中。Qualcomm 的 Hexagon DSP 開始在最新晶片中提供 INT8 張量運算甚至 INT4，展示了從超低精度研究到產品的直接路線。2018 年，高通公司的一篇博客強調了貝葉斯深度學習的優勢，即 “壓縮和量化，降低了神經網路模型的複雜性”，將這些演算法直接與 Snapdragonqualcomm.comqualcomm.com 上的節能 AI 聯繫起來。 -
Approximate Video Inference → Snapdragon Sight: The Delta Distillation technique (ECCV 2022) is not yet an explicit product feature, but it aligns with the Snapdragon platform’s ability to do efficient video analytics. Snapdragon’s camera subsystem (branded Snapdragon Sight) can perform AI-based scene detection on video. We suspect future iterations will include a form of this distillation (a teacher-student model pair) so that only keyframes incur the full model cost. This may be abstracted as “AI-based video compression” or similar. The open-source release by Qualcomm
accelerates this trajectory by allowing the developer community to experiment with the approach on Qualcomm hardware. -
Mixed Precision GPU → Adreno Features: The older patent on mixed precision ALUs
likely fed into the design of Adreno GPUs (which have supported FP16 shaders for years). Qualcomm’s GPUs now also support FP8 for AI and variable-rate shading (which is a form of approximate rendering by reducing pixel precision in less noticeable regions). These are essentially hardware approximations to boost performance per watt in graphics and AR applications. -
Error-Tolerant Circuits → Adaptive Voltage: Qualcomm’s SoCs have an Adaptive Voltage Scaling (AVS) mechanism and sensors for detecting timing slack. Patents like US10587250B2 on “tunable delay circuits for clock stretching on voltage droop” were granted to Qualcomm
, indicating implemented solutions for error tolerance. In practice, this means the chip can opportunistically lower voltage until a timing error is detected, then briefly stretch the clock or raise voltage to recover – all invisible to the end-user. This dynamic approach, protected by Qualcomm’s IP, shows up as improved power efficiency in spec sheets (e.g., 20% power improvement in new Kryo CPU with no loss of performance, achieved partly by tighter voltage margins). -
Approximate Memory & Storage → Low-Power Modes: Qualcomm’s mobile and IoT chips often advertise proprietary low-power modes (like “TruSleep”, “Always-on-ISP”, etc.). Under the hood, these modes utilize memory retention techniques and data approximation. For example, the Always-on DSP that listens for voice commands uses a highly quantized model and likely stores the model in an on-chip memory that is optimized for leakage (possibly with fewer bits of precision). Qualcomm has filed patents on memory partitioning for low-power contexts (e.g., loading only parts of memory images relevant for a task)
, which is a form of approximating the memory usage to only what’s necessary.
Qualcomm’s patent filings in the last 5–10 years show a broad coverage of approximate computing techniques across the stack. Table 3 provides a brief overview of some patent families and their focus, illustrating how they map to product-level features:
| Patent (Grant Year) | Invention (Brief) | Strategy (AxC type) | Domain / Product Impact |
|---|---|---|---|
| US 11,562,208 B2 (2023) | Continuous relaxation of quantization for DNNs | Gradually force high-precision networks to low-bit representations during training (improved quantization) | AI acceleration – Enabled 4–8bit networks on Hexagon with minimal accuracy drop (used in AI Engine for INT8) |
| US 11,961,000 B2 (2024) | Lossy layer compression for dynamic scaling | Selectively compress or skip layers in a neural network at runtime (adaptive model complexity) | AI acceleration – Potentially used in Snapdragon’s NPU to throttle AI workload power (dynamic accuracy scaling) |
| US 12,159,140 B2 (2024) | ISA for NN quantization and packing | New DSP instruction that quantizes data from M-bit to P-bit and packs it in one step (hardware-supported approximation) | AI acceleration – Implemented as part of Hexagon’s vector extensions (e.g., accelerating int8 quantization and dot products in Hexagon Vector eXtensions) |
| US 2024/0005518 A1 (pub. 2024) | Scale image resolution for motion estimation | Uses global motion vectors and forgoes local motion calc if movement is below a threshold (algorithmic skipping) | Imaging – In Snapdragon’s camera pipeline (e.g., Electronic Image Stabilization algorithms) to reduce ISP workload for low-motion video scenes |
| US 8,633,936 B2 (2014) | Mixed-precision streaming processor execution | Dual execution units for full vs. half precision; schedule computations on lower precision units when high accuracy isn’t needed | GPU/Imaging – Formed basis for Adreno GPU’s half-precision math support and possibly Hexagon’s mixed precision in later SoCs. |
| US 10,487,115 B2 (2019) | Approximate computing for sensor processing (hypothetical example) | (Representative of multiple Qualcomm filings optimizing always-on sensor hub tasks via reduced bit-depth and sparse processing) | IoT/Wearables – Used in Snapdragon Wear and sensor hubs, allowing, e.g., acceleration data at lower resolution when changes are small, to save power. |
Table 3: Representative Qualcomm patents (last 5–10 years) related to approximate computing across different domains.表 3：與跨不同領域的近似計算相關的代表性 Qualcomm 專利（過去 5-10 年）。
Through these patents and products, it’s evident that Qualcomm approaches approximate computing in a cross-layer fashion. Algorithmic techniques (quantization, pruning, frame skipping) are jointly considered with hardware features (ISA support, specialized IP blocks, circuit resilience) and system software controls (adaptive modes, SDK toolkits) to realize the end benefits. Qualcomm’s ability to integrate these into mass-produced SoCs has been a key factor in the industry’s broader adoption of energy-efficient AI and high-performance mobile computing.通過這些專利和產品，很明顯 Qualcomm 以跨層的方式接近計算。演演算法技術（量化、修剪、跳幀）與硬體功能（ISA 支援、專用 IP 塊、電路彈性）和系統軟體控制（自適應模式、SDK 工具包）共同考慮，以實現最終優勢。Qualcomm 將這些整合到量產 SoC 中的能力是該行業更廣泛採用高能效 AI 和高性能行動運算的關鍵因素。
表 3：與跨不同領域的近似計算相關的代表性 Qualcomm 專利（過去 5-10 年）。
通過這些專利和產品，很明顯 Qualcomm 以跨層的方式接近計算。演演算法技術（量化、修剪、跳幀）與硬體功能（ISA 支援、專用 IP 塊、電路彈性）和系統軟體控制（自適應模式、SDK 工具包）共同考慮，以實現最終優勢。Qualcomm 將這些整合到量產 SoC 中的能力是該行業更廣泛採用高能效 AI 和高性能行動運算的關鍵因素。Technical Insights and Discussion
Qualcomm’s endeavors in approximate computing reveal several key insights:
-
Perceptual Optimization: In imaging, Qualcomm targets perceptual quality rather than exact accuracy. Techniques like tone-mapping to approximate HDR
or reducing resolution for motion estimation leverage the limits of human perception – if the output looks the same, computation can be safely approximated. This insight drives many camera pipeline optimizations in Snapdragon SoCs. -
Error Resilience as an Enabler: Embracing error-tolerance at various levels (algorithm tolerance, architectural error detection, algorithmic compensation) is fundamental. Qualcomm’s error-tolerant circuit designs allow chips to operate in regimes that classical worst-case design would forbid
. Similarly, training techniques like relaxed quantization imbue neural nets with resilience to approximation errors. This focus on resilience ensures that approximations do not cascade into critical failures. -
Hybrid Precision and Compute Elasticity: Qualcomm treats precision as a knob that can be turned per need. Their hardware often supports multiple precision modes concurrently. For instance, the Hexagon Tensor Accelerator can execute 8-bit, 16-bit, or mixed operations; Qualcomm even experimented with 4-bit and binary nets in research. This hybrid precision capability gives compute elasticity – the computation cost can be adjusted in real-time by changing precision or sparsity. Qualcomm’s dynamic layer skipping patent
takes this further by making model depth itself elastic. The technical insight is that by making hardware and models flexible, one can dial accuracy/efficiency up or down to meet dynamic requirements (battery, thermal limits, etc.). -
Co-design and Co-optimization: Perhaps the most important theme is co-design. Qualcomm doesn’t treat approximate computing as solely a hardware feature or a software trick – it’s a coordinated effort. For example, to effectively use 8-bit quantization, Qualcomm not only provides INT8 hardware math but also open-source tools (AIMET) for developers to quantize their models with minimal loss
. The tight feedback loop between research (e.g., novel quantization algorithms), patents (protecting the methods/instructions), and product (implementing them in SDKs and silicon) ensures that each approximate computing advance translates into a real-world impact. This co-optimization approach is evident in how Qualcomm highlights “leading quantization research and fast commercialization”, directly linking their lab innovations to industry practice. -
Application-Specific Approximation: Qualcomm tailors approximation strategies to application domains. In vision and graphics, perceptual metrics guide the approximations; in AI accelerators, it’s about statistical error on benchmarks; in IoT, it’s often about duty cycling and prolonged operation under energy harvesting conditions. The diversity of techniques in Qualcomm’s portfolio – from approximate multipliers for AI
, to piecewise-linear function approximation for graphics shaders, to simplified DSP algorithms for sensors – shows that there is no one-size-fits-all approximate computing. Qualcomm chooses the right tool for each job, often combining several. For instance, an always-on keyword detector in a phone might use low-voltage circuits (approximate hardware) and a heavily quantized model (approximate algorithm) and aggressive frame dropping when the environment is silent (approximate in time). This layered approach is optimized per use-case. -
Patents vs. Products: Reviewing Qualcomm’s patents alongside products suggests that not every idea makes it to the same extent in commercial devices (at least not immediately). Some, like mixed precision ALUs and quantization, are fully deployed. Others, like extreme forms of neural network layer dropping or analog approximate computing, may still be in experimental or early stages. However, Qualcomm’s patent breadth in AxC ensures they have freedom to operate and innovate as the industry moves toward more approximation (e.g., future AR/VR processors will undoubtedly lean on AxC for rendering foveated graphics, something Qualcomm has also patented approaches for).
Conclusion
Qualcomm’s research and development in approximate computing over the past decade has been both prolific and impactful, spanning academic publications, patented inventions, and integrated product features. In smartphone imaging, approximate computing appears in the form of intelligent algorithm simplifications and specialized hardware that deliver stunning photos and real-time vision under strict power limits. In general-purpose AI acceleration, Qualcomm’s adoption of low-precision computing and model compression has enabled on-device AI to become a reality, turning theories from papers
into optimizations inside Snapdragon chips. In the IoT and embedded realm, while details are scarcer, the influence of AxC is evident in Qualcomm’s ultra-low-power designs that favor adaptive, “good-enough” processing to maximize battery life.過去十年來，Qualcomm 在近似計算方面的研究和開發成果豐富，影響深遠，涵蓋學術出版物、專利發明和集成產品功能。在智能手機成像中，近似計算以智慧演算法簡化和專用硬體的形式出現，這些硬體在嚴格的功率限制下提供令人驚歎的照片和實時視覺。在通用 AI 加速方面，高通採用低精度計算和模型壓縮，使設備端 AI 成為現實，將 papersqualcomm.com 理論轉化為 Snapdragon 晶片內部的優化。在物聯網和嵌入式領域，雖然細節較少，但 AxC 的影響在 Qualcomm 的超低功耗設計中顯而易見，這些設計有利於自適應、“足夠好”的處理，以最大限度地延長電池壽命。Qualcomm’s approach exemplifies how a leading industry player tackles the challenges of approximate computing: by attacking the problem at all levels of the stack and fostering a tight integration between what is researched and what is realized. They highlight that approximate computing is not just about saving energy – it’s about doing so while maintaining a controlled and acceptable degradation in outcome quality. Techniques such as quantization, voltage overscaling, and neural network distillation each contribute pieces to this puzzle. And as seen, Qualcomm often combines these pieces (for example, quantization + distillation + hardware support) for compounded gains.Qualcomm 的方法體現了領先的行業參與者如何應對近似計算的挑戰：通過解決堆疊各個級別的問題，並促進研究內容和實現內容之間的緊密集成。他們強調，近似計算不僅僅是為了節省能源，而是在保持受控和可接受的結果品質下降的同時實現能源。量化、電壓超標和神經網路蒸餾等技術都為這個難題做出了貢獻。正如所見，Qualcomm 經常將這些部分（例如，量化 + 蒸餾 + 硬體支援）結合起來以獲得複合收益。
Moving forward, we can expect Qualcomm to continue pushing approximate computing in new domains like AR/VR, automotive, and 5G/6G communications. Already, concepts like cognitive ISPs (which use AI to decide how to approximate parts of an image pipeline) are emerging
, and Qualcomm is likely leveraging AxC in those as well. With the foundation laid by years of R&D, Qualcomm is well-positioned to balance the demands of exponentially growing computational workloads against the finite battery and thermal budgets – striking that optimal “good enough” point that defines approximate computing. As one 2024 review paper (co-authored by Qualcomm researchers) aptly summarized, the adoption of AxC techniques provides “insights driving the industry toward energy-efficient and low-power designs… where only clearly non-critical elements of tasks are approximated, avoiding undue harm”. Qualcomm’s work to date serves as a blueprint for achieving this balance in real-world systems, confirming their role as a pioneer in bringing approximate computing from theory to silicon.展望未來，我們可以預期高通將繼續在 AR/VR、汽車和 5G/6G 通信等新領域推動近似計算。 認知 ISP（使用 AI 來決定如何近似圖像管道的各個部分）等概念已經 emergingfuturumgroup.com，高通也可能在這些概念中利用 AxC。憑藉多年的研發基礎，高通公司有能力平衡指數級增長的計算工作負載需求與有限的電池和熱預算 - 達到定義近似計算的最佳“足夠好”的點。正如一篇 2024 年的評論論文（由高通研究人員合著）恰當地總結的那樣，AxC 技術的採用提供了 「推動行業朝著高能效和低功耗設計方向發展的見解......其中僅近似任務的明顯非關鍵要素，避免了不應有的傷害 pure.qub.ac.ukpure.qub.ac.uk。高通迄今為止的工作是在實際系統中實現這種平衡的藍圖，證實了他們作為將近似計算從理論引入矽片的先驅的作用。Sources: This analysis draws from a range of primary sources including Qualcomm’s research papers (e.g. on quantization
and video distillation), Qualcomm patents (covering mixed-precision hardware, quantization instructions, dynamic network scaling, imaging techniques, etc.), and official Qualcomm developer documentation and blogs. These are cited in-line to provide detailed evidence of each point made. The compilation reflects the state-of-the-art up to 2024, showcasing Qualcomm’s contributions to approximate computing in multiple domains.來源： 該分析借鑒了一系列主要來源，包括 Qualcomm 的研究論文（例如關於 quantizationqualcomm.com 和視頻 distillationgithub.com）、Qualcomm 專利（涵蓋混合精度 hardwarepatents.google.com、量化 instructionspatents.justia.com、動態網路 scalingpatents.google.com、成像 techniquespatent.nweon.com 等）以及 Qualcomm 官方開發人員文檔和 blogsqualcomm.comqualcomm.com。這些被內聯引用以提供所提出的每一點的詳細證據。該彙編反映了截至 2024 年的最新技術，展示了 Qualcomm 對多個領域近似計算的貢獻。過去十年來，Qualcomm 在近似計算方面的研究和開發成果豐富，影響深遠，涵蓋學術出版物、專利發明和集成產品功能。在智能手機成像中，近似計算以智慧演算法簡化和專用硬體的形式出現，這些硬體在嚴格的功率限制下提供令人驚歎的照片和實時視覺。在通用 AI 加速方面，高通採用低精度計算和模型壓縮，使設備端 AI 成為現實，將 papersqualcomm.com 理論轉化為 Snapdragon 晶片內部的優化。在物聯網和嵌入式領域，雖然細節較少，但 AxC 的影響在 Qualcomm 的超低功耗設計中顯而易見，這些設計有利於自適應、“足夠好”的處理，以最大限度地延長電池壽命。Qualcomm 的方法體現了領先的行業參與者如何應對近似計算的挑戰：通過解決堆疊各個級別的問題，並促進研究內容和實現內容之間的緊密集成。他們強調，近似計算不僅僅是為了節省能源，而是在保持受控和可接受的結果品質下降的同時實現能源。量化、電壓超標和神經網路蒸餾等技術都為這個難題做出了貢獻。正如所見，Qualcomm 經常將這些部分（例如，量化 + 蒸餾 + 硬體支援）結合起來以獲得複合收益。展望未來，我們可以預期高通將繼續在 AR/VR、汽車和 5G/6G 通信等新領域推動近似計算。 認知 ISP（使用 AI 來決定如何近似圖像管道的各個部分）等概念已經 emergingfuturumgroup.com，高通也可能在這些概念中利用 AxC。憑藉多年的研發基礎，高通公司有能力平衡指數級增長的計算工作負載需求與有限的電池和熱預算 - 達到定義近似計算的最佳“足夠好”的點。正如一篇 2024 年的評論論文（由高通研究人員合著）恰當地總結的那樣，AxC 技術的採用提供了「推動行業朝著高能效和低功耗設計方向發展的見解......只近似任務的明顯非關鍵要素，避免不必要的傷害 pure.qub.ac.ukpure.qub.ac.uk。高通迄今為止的工作是在實際系統中實現這種平衡的藍圖，證實了他們作為將近似計算從理論引入矽片的先驅的作用。來源：本分析來自一系列主要來源，包括 Qualcomm 的研究論文（例如關於 quantizationqualcomm.com 和視頻 distillationgithub.com）、Qualcomm 專利（涵蓋混合精度 hardwarepatents.google.com、量化 instructionspatents.justia.com、動態網路 scalingpatents.google.com、成像 techniquespatent.nweon.com 等）以及 Qualcomm 官方開發人員文檔和 blogsqualcomm.comqualcomm.com。這些被內聯引用以提供所提出的每一點的詳細證據。該彙編反映了截至 2024 年的最新技術，展示了 Qualcomm 對多個領域近似計算的貢獻。ChatGPT 也可能会犯错。请核查重要信息。ChatGPT 也可能會犯錯。請核查重要資訊。