Enabling Long Chain Lengths and High-Throughput for Time-Domain Neuromorphic Computing

This brief investigates the limitations on throughput and delay chain length in time-domain neuromorphic computing and proposes solutions to overcome these challenges. Through the employment of clock optimization, cascaded delay cells, and pulse-width expander cells, this brief demonstrates significant throughput and scalability improvements over the current state of the art. Analyses reveal critical relationships between chain length, variability, and supply voltage, highlighting that increasing chain length improves variability and reduces sensitivity to supply scaling. Applied to a CNN, tested on the MNIST dataset, the proposed designs maintain robust accuracy with less than 1% degradation across a range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma /\mu $ </tex-math></inline-formula> values, even under weight variation. The system achieves 0.57 fJ energy/op, with throughput as high as 2432 GOPS. The findings underscore the advantage of these delay cells for high-throughput, low variability, and energy-efficient time-domain neuromorphic systems.