HDBinaryCore: A 28nm 2048-bit Hyper-Dimensional biosignal classifier achieving 25 nJ/prediction for EMG hand-gesture recognition

Hyper-Dimensional Computing (HDC), a nanoscalable learning paradigm for low-energy predictions and lightweight models, has seen a surge in interest from the hardware accelerator community. Its statistical and distributed data representation leads to highly-efficient classifiers with inherent robustness to representation errors. A digital, 28nm CMOS chip, representing the first programmable HDC biosignal processor, achieves 25.6 nJ/pred. on a leading EMG gesture recognition dataset. Measurements confirm the high robustness of HDC: a 47% bit error-rate in the datapath by VDD overscaling leads to only 1.37% accuracy drop. This realization is the most efficient and robust EMG gesture classifier to date –its per-channel efficiency is $ 1312\times$ that of Artificial Neural Networks and 76 $\mathrm{m}\mathrm{i}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{o}\mathrm{n}\times$ that of Spiking Neural Networks.