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.
Pengfei Sun, Yansong Chua, Paul Devos, and Dick Botteldooren
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FPGA hardware for event-graph NN achieves 92.7% accuracy on SHD dataset with fewer parameters than SOTA while outperforming prior FPGA SNNs.
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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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Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA
FPGA hardware for event-graph NN achieves 92.7% accuracy on SHD dataset with fewer parameters than SOTA while outperforming prior FPGA SNNs.