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A 1.041-Mb/mm<sup>2</sup> 27.38-TOPS/W Signed-INT8 Dynamic-Logic-Based ADC-less SRAM Compute-in-Memory Macro in 28nm with Reconfigurable Bitwise Operation for AI and Embedded Applications

Bonan Yan, Jeng-Long Hsu, Pang-Cheng Yu, Chia‐Chi Lee, Yaojun Zhang, Wenshuo Yue, Guoqiang Mei, Yuchao Yang, Yue Yang, Hai Li, Yiran Chen, Ru Huang

20222022 IEEE International Solid- State Circuits Conference (ISSCC)133 citationsDOI

Abstract

Advanced intelligent embedded systems perform cognitive tasks with highly-efficient vector-processing units for deep neural network (DNN) inference and other vector-based signal processing using limited power. SRAM-based compute-in-memory (CIM) achieves high energy efficiency for vector-matrix multiplications, offers <1ns read/write speed, and saves vastly repeating memory accesses. However, prior SRAM CIM macros require a large area for compute circuits (either using ADC for analog CIM [1– 4] or CMOS static logic for all-digital CIM [5–6]), have limited CIM functions, and use fixed vector-processing dimensions that cause a low-spatial-utilization rate when deploying DNN (Fig. 11.7.1).

Topics & Concepts

Static random-access memoryComputer scienceCMOSComputer hardwareArtificial neural networkParallel computingBitwise operationDigital signal processingMultiplication (music)MacroArithmeticElectronic engineeringArtificial intelligenceEngineeringMathematicsCombinatoricsProgramming languageAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesLow-power high-performance VLSI design
A 1.041-Mb/mm<sup>2</sup> 27.38-TOPS/W Signed-INT8 Dynamic-Logic-Based ADC-less SRAM Compute-in-Memory Macro in 28nm with Reconfigurable Bitwise Operation for AI and Embedded Applications | Litcius