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概率计算及混合概率计算OA北大核心CSTPCD

Stochastic Computing and Hybrid Stochastic Computing

中文摘要英文摘要

非位置概率数的计算机制已经成为边缘计算片上系统的新范式.本文介绍了概率计算(stochastic com-puting)的起源、发展和目前国内外的研究现状.针对传统概率计算存在诸如计算时延长、脉冲串信息携带效率低等问题,本文提出了二进制数-概率脉冲串混合编码的混合概率数概念,并从数的表示机理上阐释了二进制数、概率数和混合概率数的数理关系,进而揭示了混合概率计算所具备的低时延、高算力和高能效比的计算特点.本文基于40 nm CMOS工艺设计混合概率深度神经网络,该神经网络芯片在内核面积仅0.73 mm×0.73 mm的条件下,设计4 544个乘累加(MAC)单元.在时钟频率400 MHz条件下,总功率为102.3 mW,其中动态功耗仅97 μW.通过ASIC芯片的实验测试表明,混合概率计算作为一种全新的颠覆性计算范式,与其他确定性、可扩展和全并行等概率计算方案相比,其能效比分别提高了50倍、2.5倍和3.26倍.

The calculation principle of non-positional stochastic number(SN)is a promising technique for realizing high-performance computing owing to its extremely low hardware cost.This paper introduces detailly the origin,develop-ment process and the domestic and foreign development present situation.However,a disadvantage of stochastic bitstream is that the computing latency,and information-carrying efficiency and so on.We presented a hybrid stochastic computing(HSC)based on a hybrid bitstream to solve these problems,which achieves a lower hardware cost,better accuracy,and fast-er speed.The HSC neural networks is fabricated by 40 nm low-power CMOS process,with a core area of 0.73 mm×0.73 mm,power of 102.3 mW and clock of 400 MHz,which has 4 544 multiply and accumulation(MAC).The proposed Hybrid stochastic computing is tested by FPGA and ASIC.Compared with other stochastic computing method,the method proposed gains 50×,2.5×,and 3.26×energy efficiency than other methods of traditional stochastic computing.

李洪革;陈宇昊;吴俊毅;宋印杰;朱新宇

北京航空航天大学电子信息工程学院,北京市 100191

计算机与自动化

概率数概率计算混合概率数混合概率计算深度神经网络能效算力

stochastic numberstochastic computinghybrid stochastic numberhybrid stochastic computingdeep neural networkenergy efficiencycomputing performance

《电子学报》 2024 (002)

基于事件驱动的概率神经网络的工作机理及关键技术

428-440 / 13

国家自然科学基金(No.62071019) National Natural Science Foundation of China(No.62071019)

10.12263/DZXB.20230222

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