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A Charge-domain 10T SRAM based In-Memory-Computing Macro for Low Energy and Highly Accurate DNN inference | IEEE Conference Publication | IEEE Xplore

A Charge-domain 10T SRAM based In-Memory-Computing Macro for Low Energy and Highly Accurate DNN inference


Abstract:

SRAM-based In-Memory-Computing (IMC) for processing multiply-accumulate (MAC) operations is one of the promising techniques to overcome von-Neumann Bottleneck. The SRAM-b...Show More

Abstract:

SRAM-based In-Memory-Computing (IMC) for processing multiply-accumulate (MAC) operations is one of the promising techniques to overcome von-Neumann Bottleneck. The SRAM-based IMC generally requires multi-bit input activation to achieve a high inference accuracy. In this paper, a charge-sharing based 10T SRAM IMC architecture is proposed which can process multibit inputs on cell array by employing bit-line parasitic capacitances without Digital to Analog Converter (DAC). The proposed DAC-less multi-bit IMC can efficiently reduce computing energy without latency overhead. The hardware implementation with 28nm CMOS process shows that the proposed SRAM based IMC macro achieves 10.1TOPS/W with 7ns inference time at 1V and 91.4% CIFAR-10 inference accuracy.
Date of Conference: 06-09 October 2021
Date Added to IEEE Xplore: 25 November 2021
ISBN Information:
Print on Demand(PoD) ISSN: 2163-9612
Conference Location: Jeju Island, Korea, Republic of

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