HybMED: A Hybrid Neural Network Training Processor With Multi-Sparsity Exploitation for Internet of Medical Things

Cloud-based training and edge-based inference modes for Artificial Intelligence of Medical Things (AIoMT) applications suffer from accuracy degradation due to physiological signal variations among patients. On-chip learning can overcome this issue by online adaptation of neural network parameters for user-specific tasks. However, existing on-chip learning processors have limitations in terms of versatility, resource utilization, and energy efficiency. We propose HybMED, which is a novel neural signal processor that supports on-chip hybrid neural network training using a composite direct feedback alignment-based paradigm. HybMED is suitable for general-purpose health monitoring AIoMT devices. It improves resource utilization and area efficiency by the reconfigurable homogeneous core with heterogeneous data flow and enhances energy efficiency by exploiting sparsity at different granularities. The chip was fabricated by TSMC 40nm process and tested in multiple physiological signal processing tasks, demonstrating an average improvement in accuracy of 41.16% following online few-shot learning. The chip demonstrates an area efficiency of 1.17 GOPS/mm${}^{2}$ and an energy efficiency of 1.58 TOPS/W. Compared to the previous state-of-the-art physiological signal processors with on-chip learning, the chip achieves a 65$\times$ improvement in area efficiency and 1.48$\times$ improvement in energy efficiency, respectively.