An FPGA implementation of a neuromorphic auditory sensor plus graph neural network achieves 87.43% accuracy on Google Speech Commands v2 with sub-35 µs latency and 1.12 W power.
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CADAD adds activity-dependent dynamic delays to SNNs, improving accuracy on speech datasets while cutting parameter count by about 50% versus prior static delay approaches.
A multiplication-free spike-time learning rule for SNNs achieves 96.5% MNIST and 84.8% Fashion-MNIST accuracy via event-driven FPGA implementation without multiplications or explicit gradients.
citing papers explorer
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End-to-End Keyword Spotting on FPGA Using Graph Neural Networks with a Neuromorphic Auditory Sensor
An FPGA implementation of a neuromorphic auditory sensor plus graph neural network achieves 87.43% accuracy on Google Speech Commands v2 with sub-35 µs latency and 1.12 W power.
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Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks
CADAD adds activity-dependent dynamic delays to SNNs, improving accuracy on speech datasets while cutting parameter count by about 50% versus prior static delay approaches.
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A Multiplication-Free Spike-Time Learning Algorithm and its Efficient FPGA Implementation for On-Chip SNN Training
A multiplication-free spike-time learning rule for SNNs achieves 96.5% MNIST and 84.8% Fashion-MNIST accuracy via event-driven FPGA implementation without multiplications or explicit gradients.