Title | ||
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CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference |
Abstract | ||
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A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference. It leverages a novel charge-domain multiply-and-accumulate (MAC) mechanism and circuitry to achieve superior linearity under process variations compared to conventional IMC designs. The adopted semi-parallel architecture efficiently stores filters from multiple CNN layers by sharing eight standard 6T SRAM cells with one charge-domain MAC circuit. Moreover, up to six levels of bit-width of weights with two encoding schemes and eight levels of input activations are supported. A 7-bit charge-injection SAR (ciSAR) analog-to-digital converter (ADC) getting rid of sample and hold (S&H) and input/reference buffers further improves the overall energy efficiency and throughput. A 65-nm prototype validates the excellent linearity and computing accuracy of CAP-RAM. A single 512×128 macro stores a complete pruned and quantized CNN model to achieve 98.8% inference accuracy on the MNIST data set and 89.0% on the CIFAR-10 data set, with a 573.4-giga operations per second (GOPS) peak throughput and a 49.4-tera operations per second (TOPS)/W energy efficiency. |
Year | DOI | Venue |
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2021 | 10.1109/JSSC.2021.3056447 | IEEE Journal of Solid-State Circuits |
Keywords | DocType | Volume |
CMOS,convolutional neural networks (CNNs),deep learning accelerator,in-memory computation,mixed-signal computation,static random-access memory (SRAM) | Journal | 56 |
Issue | ISSN | Citations |
6 | 0018-9200 | 6 |
PageRank | References | Authors |
0.49 | 0 | 9 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhiyu Chen | 1 | 8 | 1.59 |
Zhanghao Yu | 2 | 9 | 1.52 |
Qing Jin | 3 | 6 | 0.49 |
Yan He | 4 | 6 | 2.18 |
Jingyu Wang | 5 | 7 | 0.90 |
Sheng Lin | 6 | 6 | 0.82 |
Dai Li | 7 | 6 | 0.49 |
Yanzhi Wang | 8 | 7 | 1.51 |
Kuiyuan Yang | 9 | 148 | 20.89 |