采用算法-电路协同优化技术的 A183.4-nJ/Inference 152.8-μW 35 语音命令识别有线逻辑处理器

IF 2.2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE IEEE Solid-State Circuits Letters Pub Date : 2023-11-21 DOI:10.1109/LSSC.2023.3334625
Rei Sumikawa;Atsutake Kosuge;Yao-Chung Hsu;Kota Shiba;Mototsugu Hamada;Tadahiro Kuroda
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引用次数: 0

摘要

我们开发了一个 183.4-nJ/inference 的单芯片有线逻辑 DNN 处理器,它能够识别工业标准语音识别数据集(谷歌语音命令数据集)中定义的所有 35 个命令。与最先进的模拟处理器相比,经过算法和电路共同优化的处理器可识别的命令数量增加了 3.5 倍,能效提高了 1.6 倍,同时保持了较低的设计成本。通过在单个芯片上实现 16 层 DNN 所需的所有处理电路和布线(7.63 {mathrm{ mm}}^{2}$,40 纳米),无需在 DRAM/SRAM 中存储权重系数和中间数据。由于采用了所提出的架构,实现了 152.8 \mu \text{W}$的低功耗,这对于电池供电的物联网设备上的始终在线应用来说已经足够低了。
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A183.4-nJ/Inference 152.8-μW 35-Voice Commands Recognition Wired-Logic Processor Using Algorithm-Circuit Co-Optimization Technique
A 183.4-nJ/inference single-chip wired-logic DNN processor that is capable of recognizing all 35 commands defined in the industrial standard voice recognition data set (Google speech command dataset) is developed. The algorithm-circuit co-optimized processor recognizes 3.5 times more commands with 1.6 times better-energy efficiency than the state-of-the-art analog processor while keeping design cost low. By implementing all the processing circuits and wiring required for the 16-layer DNN onto a single chip ( $7.63 {\mathrm{ mm}}^{2}$ in 40 nm), the need to store weight coefficients and intermediate data in DRAM/SRAM is eliminated. Owing to the proposed architecture, a low-power consumption of $152.8 \mu \text{W}$ is achieved, which is low enough for always-on applications on battery-powered IoT devices.
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来源期刊
IEEE Solid-State Circuits Letters
IEEE Solid-State Circuits Letters Engineering-Electrical and Electronic Engineering
CiteScore
4.30
自引率
3.70%
发文量
52
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