Scaling-CIM: eDRAM In-Memory-Computing Accelerator With Dynamic-Scaling ADC and Adaptive Analog Operation

IEEE JOURNAL OF SOLID-STATE CIRCUITS(2024)

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摘要
This article presents Scaling-computing-in-memory(CIM), an energy-efficient embedded dynamic random access memory (eDRAM)-based in-memory-computing (IMC) accelerator with a dynamic-scaling readout for signal-to-quantization-noise ratio (SQNR) boosting and analog-to-digital converter(ADC) overhead reduction. It greatly saves the ADC cost by reducing the required number of ADC-bit and ADC operations by codesigning the algorithm and hardware. Scaling-CIM pro-poses three key features: 1) dynamic scaling ADC (DSA) boosts SQNR of multibit operation even with low-bit ADC; 2) adaptive analog bit-parallel (AABP) accumulation reduces the redundant ADC operation; and 3) layer-wise adaptive bit-truncation(LABT) search further enhances efficiency on benchmarks. The Scaling-CIM is fabricated in 28-nm CMOS technology and occupies a 2.03-mm(2)die area with an 800-kb eDRAM cell .It achieves 39.7-TOPS/W (8-9 b) energy efficiency on the RestNet-18 benchmark and 1.96xhigher efficiency figure of merit (FoM)than the previous IMC-based accelerator.
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关键词
Computer architecture,Microprocessors,Capacitors,Sensors,Costs,Hardware,Energy efficiency,Deep learning,embedded dynamic random access memory (eDRAM),in-memory computing (IMC)
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