A 332 TOPS/W Input/Weight-Parallel Computingin-Memory Processor with Voltage-Capacitance-Ratio Cell and Time-Based ADC

被引:2
作者
Hong, Seongyon [1 ]
Um, Soyeon [1 ]
Kim, Sangjin [1 ]
Kim, Sangyeob [1 ]
Jo, Wooyoung [1 ]
Yoo, Hoi-Jun [1 ]
机构
[1] Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon, South Korea
来源
2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS | 2023年
关键词
Computing-in-Memory (CIM); deep neural network (DNN); energy efficiency; SRAM; time-domain ADC; MACRO;
D O I
10.1109/ISCAS46773.2023.10181902
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recent computing-in-memory (CIM) achieves high energy efficiency with charge-domain computation and multi-bit input driving. However, the previous works still require high power consumption and trade computation signal-to-noise ratio (SNR) for energy efficiency. This work proposes an energy-efficient and accurate multi-bit input/weight-parallel CIM processor with four key features: 1) a 10T2C sign-magnitude cell with voltage-capacitance-ratio ( VCR) decoding for 5-bit analog inputs with only 2-level supply voltages, 2) a computation word line (CWL) charge reuse method for input driver power reduction, 3) a signal-amplifying noise canceling voltage-to-time converter (SANC-VTC) for SNR improvement, and 4) a distribution-aware time-to-digital converter (DA-TDC) for ADC power reduction. The proposed CIM processor is simulated in 28 nm CMOS technology with 1.25 mm2 area. As a result, it achieves 4.44 mW power consumption and 332 TOPS/W energy efficiency with 72.43% benchmark accuracy (@ ImageNet, ResNet50, 5-bit input/5-bit weight).
引用
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页数:5
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