This letter presents the first on-chip bio-inspired reservoir computer (RC) prototype implemented in a 65-nm CMOS. The RC comprises 50 time-multiplexed neurons, and each neuron embeds a strong nonlinearity in a feedback loop. The RC applies a nonlinear transformation to the input and projects it to high-dimensional space, thus allowing linear separation by a simple logistic-regression (LR) layer implemented off-chip. We demonstrate real-time stress detection from electrocardiogram (ECG) signals using the RC. The RC achieves 93% classification accuracy which is 6% better than the state-of-the-art digital classifiers. Operating at 40 kHz, the prototype consumes 27.5 nJ/classification which is 7× lower than the state-of-the-art ECG processors performing similar complexity classification task.
- Electrocardiogram (ECG) sensor
- machine learning (ML)
- reservoir computing
- stress detection
ASJC Scopus subject areas
- Electrical and Electronic Engineering