Low-power Spike-based Wearable Analytics on RRAM Crossbars
This work addresses energy and latency constraints for wearable devices by improving SNN adaptation on RRAM hardware, though it is incremental as it builds on existing DFA and SNN methods.
The paper tackles the problem of adapting Spiking Neural Networks (SNNs) on RRAM crossbars for wearable analytics by proposing online adaptation using Direct Feedback Alignment (DFA) instead of backpropagation, resulting in up to 64.1% lower energy consumption, 10.1% lower area overhead, 2.1x lower latency, and 7.55% higher accuracy on human activity recognition tasks.
This work introduces a spike-based wearable analytics system utilizing Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based on RRAM crossbars, which are known for their compactness and energy-efficiency. Given the hardware constraints and noise characteristics of the underlying RRAM crossbars, we propose online adaptation of pre-trained SNNs in real-time using Direct Feedback Alignment (DFA) against traditional backpropagation (BP). Direct Feedback Alignment (DFA) learning, that allows layer-parallel gradient computations, acts as a fast, energy & area-efficient method for online adaptation of SNNs on RRAM crossbars, unleashing better algorithmic performance against those adapted using BP. Through extensive simulations using our in-house hardware evaluation engine called DFA_Sim, we find that DFA achieves upto 64.1% lower energy consumption, 10.1% lower area overhead, and a 2.1x reduction in latency compared to BP, while delivering upto 7.55% higher inference accuracy on human activity recognition (HAR) tasks.