A Delta-Sigma-Based Computing-In-Memory Macro Targeting Edge Computation

Many applications of machine learning (ML) have been integrated into edge devices with their low communication latency. In edge computation, the reprocessing of redundant data results in considerable energy waste. The prior research utilized a digital-delta-digital-sigma computing-in-memory (CIM) scheme to mitigate this redundancy. However, the 7-bit LSB-first ADC resulting from the near-zero-mean output distribution led to excessive area and latency overhead. The following digital adder further induced power consumption and latency. We propose a digital-delta-analog-sigma CIM macro incorporating an analog sigma converter (SC) for edge computation, involving a switch-capacitor integrator with a floating inverter amplifier (FIA) and a quantizer. The increased analog swing of the sigma integrator leads to the expanded output distribution, thereby maintaining comparable accuracy with a relaxed quantizer resolution. The simulation demonstrates that our strategy contributes to a 57.5% reduction in latency, a resolution decrease of 2 bits, and better energy efficiency. These improvements can potentially enhance energy efficiency and computational speed in edge computation devices.