SpikON introduces learnable threshold and weight techniques plus a dual-parallel SNN accelerator that cut training latency by 32% and energy by 35% while delivering 7-27x throughput gains over Apple M4 GPU and TPU-like designs.
One timestep is all you need: Training spiking neural networks with ultra low latency
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.AR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A 28nm CMOS subthreshold SRAM-based CIM macro for SNNs with in-situ regulation achieves 93.64% accuracy on keyword spotting, 1181.42 TOPS/W energy efficiency, and 7.24 TOPS/mm² density.
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SpikON: A Dual-Parallel and Efficient Accelerator for Online Spiking Neural Networks Learning
SpikON introduces learnable threshold and weight techniques plus a dual-parallel SNN accelerator that cut training latency by 32% and energy by 35% while delivering 7-27x throughput gains over Apple M4 GPU and TPU-like designs.
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A PVT-Resilient Subthreshold SRAM-Based In-Memory Computing Accelerator with In-Situ Regulation for Energy-Efficient Spiking Neural Networks
A 28nm CMOS subthreshold SRAM-based CIM macro for SNNs with in-situ regulation achieves 93.64% accuracy on keyword spotting, 1181.42 TOPS/W energy efficiency, and 7.24 TOPS/mm² density.