REVIEW
An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present an on-chip trainable neuron circuit. Our proposed circuit suits bio-inspired spike-based time-dependent data computation for training spiking neural networks (SNN). The thresholds of neurons can be increased or decreased depending on the desired application-specific spike generation rate. This mechanism provides us with a flexible design and scalable circuit structure. We demonstrate the trainable neuron structure under different operating scenarios. The circuits are designed and optimized for the MIT LL SFQ5ee fabrication process. Margin values for all parameters are above 25\% with a 3GHz throughput for a 16-input neuron.
Discussion (0). Continue with ORCID to comment.