Cross-Layer Error Modeling and Deployment of Compressed Sensing Object Detection on Compute-in-Memory Systems

Abstract

Deploying deep neural networks on compute-in-memory (CIM) hardware is challenged by analog non-idealities such as device nonlinearity and circuit-level noise. This paper proposes a cross-layer framework that enables accurate and efficient deployment of a compressed sensing-based object detection (CSDet) model on a CIM platform built with programmable linear random-access memory. The framework incorporates device- and circuit-level error modeling into the training and deployment flow, enabling slope-aware compensation and statistical correction of residual errors. Experimental results show that the calibrated CSDet achieves up to 11.3% improvement in detection accuracy with minimal hardware overhead, demonstrating the practicality of robust inference on non-ideal CIM systems.