A High-Efficiency Neural Processing SoC for Adaptive Closed-Loop Neuromodulation

Adaptive closed-loop neuromodulation is an emerging therapeutic paradigm for treating neurological and psychiatric diseases. However, its hardware implementation remains challenged by limited regulation accuracy, high latency & energy overhead, and poor cross-workload compatibility. To address these faced challenges, this article presents a multi-task neural processing system-on-chip (SoC) with three key technologies. First, an established multi-input multi-output linear state-space model (MIMO LSSM) with linear quadratic Gaussian (LQG) control is configured for deterministic on-chip execution to improve regulation accuracy. Second, a processing element (PE)-array-aware compact parameter-encoding scheme is proposed to reduce storage cost and memory-access overhead. Third, a mode-configurable compute fabric (MCCF) is designed to support diverse neuromodulation workloads on a unified hardware fabric. The designed SoC was fabricated in a TSMC 65nm CMOS process. Measured results and performance comparison show that it achieves a maximum energy efficiency of 1.43 TOPS/W (3.08×), a maximum area efficiency of 1.09 GOPS/mm2 (13.29×), and a peak performance of 5.12 GOPS (40.96×). Besides, the SoC has been demonstrated on the BONN and DEAP datasets, achieving accuracies of 99.18% in seizure detection and 92.3% in emotion detection. Overall, the proposed SoC offers a competitive hardware solution for adaptive closed-loop neuromodulation.