Abstract:
We design a platform for prototyping low-cost analog neural networks for on-chip integration with analog/RF circuits. Integration supports self-test, self-tuning, and trust/aging monitoring by classifying analog measurements from on-chip sensors. 1) Low energy and area budgets of neural network circuits; 2) robust learning in the presence of analog inaccuracies; and 3) long-term retention of learned functionality are prioritized. Our chip uses sub-μW power and a reconfigurable array of synapses and neurons below threshold. Dual-mode weight storage in the synapse circuits allows for fast bidirectional weight updates during training and permanent storage of learned functionality. We discuss a robust learning strategy and evaluate the system on benchmark problems like XOR2-6 and two-spirals classification.
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