A 94.76%-accuracy, <16μs/classification-latency STT-MRAM CiM-based Classifier for Closed-loop Peripheral Nerve Modulation Implants

This paper presents a novel, low-latency, and power-efficient peripheral nervous system (PNS) on-chip classifier for real-time closed-loop adaptive neuromodulation. We propose an end-to-end closed-loop modulation system with a novel compute-in-memory (CiM) architecture based on magnetoresistive random-access memory (MRAM), enabling on-chip classification of neural signals. Using a custom deep neural network (DNN) and an end-to-end kernel fusion technique, the interface achieves minimal latency and power consumption, eliminating the need for external processors. This system is implemented in a 28-nm FD-SOI process, achieving 94.76% accuracy and <16μs/classification latency. This technology enables next-generation neuroprosthetic and therapeutic devices by providing a robust solution for real-time in-situ PNS classification.