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Open problems in Efficient spiking neural

Efficient quantization for spiking neural networks1 paper raises it · 1 addresses it
Low-bit weights and membrane potentials are needed for neuromorphic hardware but existing PTQ methods ignore spike-specific errors.
- SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networksraises · addresses
Redundant subthreshold computation in SNNs1 paper raises it · 1 addresses it
Postsynaptic neurons in subthreshold states waste computation despite low firing probability.
- The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networksraises · addresses
Catastrophic forgetting in continual learning for SNNs1 paper raises it · 1 addresses it
SNNs suffer from accuracy loss when learning tasks sequentially under memory constraints.
- Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networksraises · addresses
Hardware-aware training and inference for SNNs1 paper raises it · 2 address it
Efficient on-device deployment requires specialized hardware and training methods to avoid memory and latency overhead.
- An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classificationraises · addresses
- Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networksaddresses
Accuracy gap in spiking language models1 paper raises it · 1 addresses it
Spiking language models lag behind dense models in accuracy, with a persistent loss gap even at scale.
- Compute-Optimal Scaling Laws for Spiking Language Modelsraises · addresses

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efficient spiking neural · spiking neural networks · networks spiking neural39 papers2 this week

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arXiv cs.CVOct 15

The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks

Ruichen Ma, Yicai Chen, Pujun Zhou, Yue Zuo, Chongyang Li +3

Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-driven sparsity. However, a substantial yet chronically overlooked source of computational overhead persists: the continuous subthreshold dynamics of postsynaptic neurons. Quantitative analysis reveals that up to 44% of neurons in an ImageNet-trained ResNet-18 model reside in a deeply inhibited state, imposing a persistent computational burden despite having a negligible probability of firing. To address this inefficiency, the Dormant Spiking Neuron (DSN) model is introduced. By incorporating a state-driven dynamic gate, the DSN allows a neuron to enter a dormant state when its membrane potential falls below a predefined negative threshold, dynamically pruning redundant computations at the instance level. Crucially, this dormancy mechanism is directly integrated into the neuronal dynamics and trained end-to-end via backpropagation through time using surrogate gradients, enabling the network to actively learn a robust dormancy policy that adaptively preserves critical information flow. Extensive experiments across static and…

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efficient spiking neural · spiking neural networks · networks spiking neural39 papers2 this week

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arXiv cs.CVOct 15

The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks

Ruichen Ma, Yicai Chen, Pujun Zhou, Yue Zuo, Chongyang Li +3

Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-driven sparsity. However, a substantial yet chronically overlooked source of computational overhead persists: the continuous subthreshold dynamics of postsynaptic neurons. Quantitative analysis reveals that up to 44% of neurons in an ImageNet-trained ResNet-18 model reside in a deeply inhibited state, imposing a persistent computational burden despite having a negligible probability of firing. To address this inefficiency, the Dormant Spiking Neuron (DSN) model is introduced. By incorporating a state-driven dynamic gate, the DSN allows a neuron to enter a dormant state when its membrane potential falls below a predefined negative threshold, dynamically pruning redundant computations at the instance level. Crucially, this dormancy mechanism is directly integrated into the neuronal dynamics and trained end-to-end via backpropagation through time using surrogate gradients, enabling the network to actively learn a robust dormancy policy that adaptively preserves critical information flow. Extensive experiments across static and…

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Efficient spiking neural

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35 papers this month

Open problems in Efficient spiking neural

Efficient quantization for spiking neural networks1 paper raises it · 1 addresses it
Low-bit weights and membrane potentials are needed for neuromorphic hardware but existing PTQ methods ignore spike-specific errors.
- SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networksraises · addresses
Redundant subthreshold computation in SNNs1 paper raises it · 1 addresses it
Postsynaptic neurons in subthreshold states waste computation despite low firing probability.
- The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networksraises · addresses
Catastrophic forgetting in continual learning for SNNs1 paper raises it · 1 addresses it
SNNs suffer from accuracy loss when learning tasks sequentially under memory constraints.
- Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networksraises · addresses
Hardware-aware training and inference for SNNs1 paper raises it · 2 address it
Efficient on-device deployment requires specialized hardware and training methods to avoid memory and latency overhead.
- An FPGA-Based Spiking Neural Network for Spatiotemporal Pattern Learning and Classificationraises · addresses
- Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networksaddresses
Accuracy gap in spiking language models1 paper raises it · 1 addresses it
Spiking language models lag behind dense models in accuracy, with a persistent loss gap even at scale.
- Compute-Optimal Scaling Laws for Spiking Language Modelsraises · addresses

Named by an AI from these papers' abstracts.