SRAM In-Memory Computing Macro With Delta-Sigma Modulator-Based Variable-Resolution Activation
IEEE SOLID-STATE CIRCUITS LETTERS(2023)
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.
更多查看译文
关键词
Compute-in-memory (CIM),convolutional neural network (CNN),delta-sigma,static random access memory
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要