SP-PIM A Super-Pipelined Processing-In-Memory Accelerator With Local Error Predi

Abstract

Over the past few years, on-device learning (ODL) has become an integral aspect of the success of edge devices that embrace machine learning (ML) since it plays a crucial role in restoring ML model accuracy when the edge environment changes. However, implementing ODL on battery-limited edge devices poses significant challenges due to the generation of large-size intermediate data during ML training and the frequent data movement between the processor and memory, resulting in substantial power co