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
收藏
页数:5
相关论文
共 15 条
[1]  
Biswas A, 2018, ISSCC DIG TECH PAP I, P488, DOI 10.1109/ISSCC.2018.8310397
[2]   Energy minimization using multiple supply voltages [J].
Chang, JM ;
Pedram, M .
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 1997, 5 (04) :436-443
[3]   Fundamental Limits on Energy-Delay-Accuracy of In-Memory Architectures in Inference Applications [J].
Gonugondla, Sujan K. ;
Sakr, Charbel ;
Dbouk, Hassan ;
Shanbhag, Naresh R. .
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 41 (10) :3188-3201
[4]   A Programmable Neural-Network Inference Accelerator Based on Scalable In-Memory Computing [J].
Jia, Hongyang ;
Ozatay, Murat ;
Tang, Yinqi ;
Valavi, Hossein ;
Pathak, Rakshit ;
Lee, Jinseok ;
Verma, Naveen .
2021 IEEE INTERNATIONAL SOLID-STATE CIRCUITS CONFERENCE (ISSCC), 2021, 64 :236-+
[5]   A Programmable Heterogeneous Microprocessor Based on Bit-Scalable In-Memory Computing [J].
Jia, Hongyang ;
Valavi, Hossein ;
Tang, Yinqi ;
Zhang, Jintao ;
Verma, Naveen .
IEEE JOURNAL OF SOLID-STATE CIRCUITS, 2020, 55 (09) :2609-2621
[6]   C3SRAM: An In-Memory-Computing SRAM Macro Based on Robust Capacitive Coupling Computing Mechanism [J].
Jiang, Zhewei ;
Yin, Shihui ;
Seo, Jae-Sun ;
Seok, Mingoo .
IEEE JOURNAL OF SOLID-STATE CIRCUITS, 2020, 55 (07) :1888-1897
[7]   A Charge-Domain Scalable-Weight In-Memory Computing Macro With Dual-SRAM Architecture for Precision-Scalable DNN Accelerators [J].
Lee, Eunyoung ;
Han, Taeyoung ;
Seo, Donguk ;
Shin, Gicheol ;
Kim, Jaerok ;
Kim, Seonho ;
Jeong, Soyoun ;
Rhe, Johnny ;
Park, Jaehyun ;
Ko, Jong Hwan ;
Lee, Yoonmyung .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2021, 68 (08) :3305-3316
[8]  
Lee JoonHyub., 2021, P S VLSI CIRC KYOT J, P1, DOI [DOI 10.23919/VLSICIRCUITS52068.2021.9492444, 10.23919/VLSICircuits52068.2021.9492444]
[9]   Decoupling capacitors for power distribution systems with multiple power supply voltages [J].
Popovich, M ;
Friedman, EG .
IEEE INTERNATIONAL SOC CONFERENCE, PROCEEDINGS, 2004, :331-334
[10]  
Si X, 2019, ISSCC DIG TECH PAP I, V62, P396, DOI 10.1109/ISSCC.2019.8662392