ASiM: Improving Transparency of SRAM-based Analog Compute-in-Memory Research with an Open-Source Simulation Framework

Abstract: SRAM-based Analog Compute-in-Memory (ACiM) demonstrates promising energy efficiency for deep neural network (DNN) processing. Although recent aggressive design strategies have led to successive improvements on efficiency, there is limited discussion regarding the accompanying inference accuracy challenges. Given the growing difficulty in validating ACiM circuits with full-scale DNNs, standardized modeling methodology and open-source inference simulator are urgently needed. This paper presents ASiM, a simulation framework specifically designed to assess inference quality, enabling comparisons of ACiM prototype chips and guiding design decisions. ASiM works as a plug-and-play tool that integrates seamlessly with the PyTorch ecosystem, offering speed and ease of use. Using ASiM, we conducted a comprehensive analysis of how various design factors impact DNN inference. We observed that activation encoding can tolerate certain levels of quantization noise, indicating a substantial potential for bit-parallel scheme to enhance energy efficiency. However, inference accuracy is susceptible to noise, as ACiM circuits typically use limited ADC dynamic range, making even small errors down to 1 LSB significantly deteriorates accuracy. This underscores the need for high design standards, especially for complex DNN models and challenging tasks. In response to these findings, we propose two solutions: Hybrid Compute-in-Memory architecture and majority voting to secure accurate computation of MSB cycles. These approaches improve inference quality while maintaining energy efficiency benefits of ACiM, offering promising pathways toward reliable ACiM deployment in real-world applications.