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SRAM In-Memory Computing Macro With Delta-Sigma Modulator-Based Variable-Resolution Activation

IEEE SOLID-STATE CIRCUITS LETTERS(2023)

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摘要
This letter presents an SRAM-based compute-in-memory (CIM) macro that uses 1-bit $\Delta \Sigma $ modulators to convert input and output activations to binary pulse waveform. The SRAM macro uses switched-capacitors for vector matrix multiplications and together with binary input activation improves linearity compared to current-domain SRAM CIM macros and allows reconfigurable activation resolution. The proposed macro is fabricated in 65 nm and benchmarked on MNIST and CIFAR-10 datasets with accuracies of 98.67% and 89.85%, respectively, with energy-efficiency in the range of 15.4-138.6 TOPS/W.
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关键词
Compute-in-memory (CIM),convolutional neural network (CNN),delta-sigma,static random access memory
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