Authors
Kangyu Su, Zhang Qiu, Yuxiao Yang, Yueming Wang, Jian Xu
Published in
IEEE transactions on biomedical circuits and systems. Volume PP. Jun 23, 2026. Epub Jun 23, 2026.
Abstract
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.
PMID:
42335065
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.
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