A 617 TOPS/W All Digital Binary Neural Network Accelerator in 10nm FinFET CMOS

Phil C. Knag, Gregory K. Chen, H. Sumbul, Raghavan Kumar, M. Anders, Himanshu Kaul, S. Hsu, A. Agarwal, Monodeep Kar, Seongjong Kim, R. Krishnamurthy
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引用次数: 13

Abstract

A 10nm digital Binary Neural Network (BNN) chip implements 1b activations and weights for compute density of 418TOPS/mm2 and memory density of 414KB/mm2. The chip achieves an energy efficiency of 617TOPS/W by leveraging Compute Near Memory (CNM), parallel inner product compute, and Near-Threshold Voltage (NTV) operation. The digital BNN design approaches the energy efficiency of analog in-memory techniques while also ensuring deterministic, scalable, and precise operation.
617 TOPS/W全数字二进制神经网络加速器在10nm FinFET CMOS
10nm数字二进制神经网络(BNN)芯片实现1b激活和权重,计算密度为418TOPS/mm2,内存密度为414KB/mm2。该芯片利用计算近内存(CNM)、并行内积计算和近阈值电压(NTV)操作实现了617TOPS/W的能效。数字BNN设计接近模拟内存技术的能源效率,同时还确保确定性,可扩展和精确操作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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