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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 12
Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks
Somayeh Hussaini, Tobias Fischer, Michael Milford
Neuromorphic sensing and computing promise low latency and high energy efficiency for robotics, yet most event-based visual place recognition (VPR) pipelines reintroduce dense, frame-like computation downstream of an inherently sparse sensor, forfeiting the sensor's latency advantage. In this paper, we address the problem of resolving an event-based VPR query in tens of microseconds on a commodity CPU. We train a two-layer spiking neural network (SNN) with additive spike-timing-dependent plasticity (STDP), which drives the learned weight matrix towards a strongly bimodal distribution, amenable to aggressive magnitude pruning. At inference, our pipeline exploits this sparsity by reading only the pruned weights corresponding to the query's active pixels. As surviving weights share a near-uniform magnitude, each neuron's score reduces to an integer count of matching synapses instead of a weighted sum, lowering per-query latency. This computation is small enough to remain resident in a CPU's fast on-chip cache. On three event-based VPR benchmarks, our sparse SNN exceeds the recall of LENS, an existing event-based SNN, while reducing per-query latency by 5.5-6.2x to tens of…
PDF arXiv
arXiv cs.CVOct 11
SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts
Yiding Sun, Zijie Xu, Hongqiang Lin, Jiajun Cao, Dongxu Zhang +5
Multimodal 3D occupancy prediction underpins safe autonomous driving through comprehensive scene understanding. However, existing methods have largely pursued higher accuracy through sophisticated fusion, leaving resilience to missing sensors underexplored. In this paper, we investigate spiking neural networks (SNNs) as a means of building more resilient multimodal perception. Still, the discrete nature of spiking neurons makes optimization challenging. To overcome this difficulty, SpikeOcc is introduced with two complementary components, a Spiking Mixture-of-Experts (SMoE) and Sensor Availability Learning (SAL). Specifically, SMoE organizes RGB, LiDAR, and joint fusion into specialized pathways, enabling full-width unimodal representations while retaining the original multimodal features via a bounded residual. Beyond sensor dropout, SAL employs a staged curriculum with explicit routing supervision, progressively improving robustness under severe sensing conditions. Remarkably, SAL also yields notable gains for conventional models, highlighting its broader potential for resilient multimodal learning. Extensive experiments demonstrate that SpikeOcc maintains competitive…
PDF arXiv
arXiv cs.LGOct 10
Compute-Optimal Scaling Laws for Spiking Language Models
Dan Saunders, Benjamin Ren, Catherine He
Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the efficiency-accuracy tradeoff of spiking language models is uncharacterized at scale. We present the first Chinchilla-style IsoFLOP scaling study of a spiking language model, sweeping SpikeGPT-family models (RWKV backbone) from width 384 to 1536 across compute budgets from $3\times10^{17}$ to $10^{19}$ FLOPs on FineWeb-EDU. We report two findings. First, the compute-optimal frontier is $N_{opt} \propto C^{0.56}$, close to the 0.50 that Hoffmann et al. report for dense Transformers, so spiking does not change how parameters and data should be traded off under a compute budget. Second, against non-spiking RWKV and Transformer baselines trained the same way, the loss gap splits into a backbone cost of about 0.16 nats that does not change with compute and a spiking cost that falls from 0.34 to about 0.23 nats and then levels off. These results give the first compute-optimal recipe for spiking language models and quantify the accuracy cost of their sparsity.
PDF arXiv
arXiv cs.ROOct 8
SDPAD: A Fully Spike-Driven Pipeline for End-to-End Autonomous Driving
Chengjun Zhang, Yuhao Zhang, Jie Yang, Mohamad Sawan
End-to-end autonomous driving demands trajectory planners that are both highly accurate and cheap enough for edge deployment. State-of-the-art artificial neural network (ANN) planners meet the accuracy requirement at the cost of heavy dense computation, while spiking neural networks (SNNs)---though promising orders-of-magnitude energy savings through sparse, event-driven arithmetic---still lag far behind in planning accuracy. We present \textbf{SDPAD}, a fully spike-driven end-to-end planning pipeline that closes this gap. SDPAD converts a pre-trained ANN perception stack into integer-spike form via quantized ANN2SNN conversion, lifts multi-view images into the bird's-eye-view (BEV) space with a spike-driven-max (SDM) depth distribution (Spike-3D-Lift), and plans through the Spike-QFormer, a spiking query transformer in which ego, agent, and map queries distilled from the BEV scene are fused by learnable waypoint queries via cross-attention, followed by deformable spike-cross-attention refinement. Every operation is gated by integer spikes and inference is a single feed-forward pass without temporal simulation loops. On the nuScenes open-loop benchmark, SDPAD achieves an average…
PDF arXiv
arXiv cs.CVOct 8
MSGAT: Multi-Head Spiking Graph Attention with Similarity-Space Fusion for Image-Text Retrieval
Xintao Zong, Wenxuan Liu, Jianhao Ding, Zhaofei Yu, Tiejun Huang
Spiking neural networks (SNNs) offer an energy-efficient computing paradigm through sparse event-driven computation, showing great potential for efficient multimodal learning. However, applying SNNs to high-level multimodal tasks, such as image-text retrieval (ITR), remains challenging, since sparse spike representations make it difficult to capture semantic structures required for cross-modal alignment. Existing spiking ITR methods rely on local alignment and additional soft-label supervision during training, while lacking awareness of structural and multi-granularity relationships. To address these issues, we propose a Multi-head Spiking Graph Attention Network (\textbf{MSGAT}) for structural modeling and equip it with dynamic attention heads to capture complementary relational patterns and enable spike-driven graph reasoning and aggregation. However, within a two-branch multi-granularity fusion framework, the fine-grained spike representations generated by MSGAT are sparse and discrete, whereas the global representations are continuous, making conventional feature-level fusion susceptible to interference across heterogeneous representations. Therefore, we introduce…
PDF arXiv
arXiv cs.AIOct 8
SpikeSSL: A Universal Spike Inference Framework with Dynamics-Informed State-Space Layers
Chenghao Yue, Siming Xing, Shuran Liu, Angran Li, Yuanlong Zhang
Two-photon calcium imaging is a standard tool for recording large neural populations in vivo, yet inferring spikes accurately across the growing diversity of calcium indicators remains an open problem. Existing supervised methods achieve reasonable in-domain accuracy but generalize poorly to unseen indicators, because different indicators induce distinct fluorescence kinetics and signal statistics while existing architectures remain relatively simple generic temporal regressors without dynamics-matched inductive bias. We propose SpikeSSL, a universal spike inference framework whose temporal backbone is a bank of bidirectional IIR state-space layers broadly motivated by calcium dynamics. A multi-modal conditioning encoder maps indicator identity, sampling rate, and trace-level signal statistics into a global conditioning vector that modulates the backbone via Adaptive Layer Normalization, while a heteroscedastic variance head provides calibrated per-frame uncertainty. On a benchmark with five fixed evaluation splits built from 33 public ground-truth datasets, SpikeSSL achieves state-of-the-art performance in both in-domain and zero-shot leave-one-indicator-out settings. We also…
PDF arXiv

Papers

Topics in this field

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

Recent papers

All in Research →

arXiv cs.CVOct 12
Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks
Somayeh Hussaini, Tobias Fischer, Michael Milford
Neuromorphic sensing and computing promise low latency and high energy efficiency for robotics, yet most event-based visual place recognition (VPR) pipelines reintroduce dense, frame-like computation downstream of an inherently sparse sensor, forfeiting the sensor's latency advantage. In this paper, we address the problem of resolving an event-based VPR query in tens of microseconds on a commodity CPU. We train a two-layer spiking neural network (SNN) with additive spike-timing-dependent plasticity (STDP), which drives the learned weight matrix towards a strongly bimodal distribution, amenable to aggressive magnitude pruning. At inference, our pipeline exploits this sparsity by reading only the pruned weights corresponding to the query's active pixels. As surviving weights share a near-uniform magnitude, each neuron's score reduces to an integer count of matching synapses instead of a weighted sum, lowering per-query latency. This computation is small enough to remain resident in a CPU's fast on-chip cache. On three event-based VPR benchmarks, our sparse SNN exceeds the recall of LENS, an existing event-based SNN, while reducing per-query latency by 5.5-6.2x to tens of…
PDF arXiv
arXiv cs.CVOct 11
SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts
Yiding Sun, Zijie Xu, Hongqiang Lin, Jiajun Cao, Dongxu Zhang +5
Multimodal 3D occupancy prediction underpins safe autonomous driving through comprehensive scene understanding. However, existing methods have largely pursued higher accuracy through sophisticated fusion, leaving resilience to missing sensors underexplored. In this paper, we investigate spiking neural networks (SNNs) as a means of building more resilient multimodal perception. Still, the discrete nature of spiking neurons makes optimization challenging. To overcome this difficulty, SpikeOcc is introduced with two complementary components, a Spiking Mixture-of-Experts (SMoE) and Sensor Availability Learning (SAL). Specifically, SMoE organizes RGB, LiDAR, and joint fusion into specialized pathways, enabling full-width unimodal representations while retaining the original multimodal features via a bounded residual. Beyond sensor dropout, SAL employs a staged curriculum with explicit routing supervision, progressively improving robustness under severe sensing conditions. Remarkably, SAL also yields notable gains for conventional models, highlighting its broader potential for resilient multimodal learning. Extensive experiments demonstrate that SpikeOcc maintains competitive…
PDF arXiv
arXiv cs.LGOct 10
Compute-Optimal Scaling Laws for Spiking Language Models
Dan Saunders, Benjamin Ren, Catherine He
Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the efficiency-accuracy tradeoff of spiking language models is uncharacterized at scale. We present the first Chinchilla-style IsoFLOP scaling study of a spiking language model, sweeping SpikeGPT-family models (RWKV backbone) from width 384 to 1536 across compute budgets from $3\times10^{17}$ to $10^{19}$ FLOPs on FineWeb-EDU. We report two findings. First, the compute-optimal frontier is $N_{opt} \propto C^{0.56}$, close to the 0.50 that Hoffmann et al. report for dense Transformers, so spiking does not change how parameters and data should be traded off under a compute budget. Second, against non-spiking RWKV and Transformer baselines trained the same way, the loss gap splits into a backbone cost of about 0.16 nats that does not change with compute and a spiking cost that falls from 0.34 to about 0.23 nats and then levels off. These results give the first compute-optimal recipe for spiking language models and quantify the accuracy cost of their sparsity.
PDF arXiv
arXiv cs.ROOct 8
SDPAD: A Fully Spike-Driven Pipeline for End-to-End Autonomous Driving
Chengjun Zhang, Yuhao Zhang, Jie Yang, Mohamad Sawan
End-to-end autonomous driving demands trajectory planners that are both highly accurate and cheap enough for edge deployment. State-of-the-art artificial neural network (ANN) planners meet the accuracy requirement at the cost of heavy dense computation, while spiking neural networks (SNNs)---though promising orders-of-magnitude energy savings through sparse, event-driven arithmetic---still lag far behind in planning accuracy. We present \textbf{SDPAD}, a fully spike-driven end-to-end planning pipeline that closes this gap. SDPAD converts a pre-trained ANN perception stack into integer-spike form via quantized ANN2SNN conversion, lifts multi-view images into the bird's-eye-view (BEV) space with a spike-driven-max (SDM) depth distribution (Spike-3D-Lift), and plans through the Spike-QFormer, a spiking query transformer in which ego, agent, and map queries distilled from the BEV scene are fused by learnable waypoint queries via cross-attention, followed by deformable spike-cross-attention refinement. Every operation is gated by integer spikes and inference is a single feed-forward pass without temporal simulation loops. On the nuScenes open-loop benchmark, SDPAD achieves an average…
PDF arXiv
arXiv cs.CVOct 8
MSGAT: Multi-Head Spiking Graph Attention with Similarity-Space Fusion for Image-Text Retrieval
Xintao Zong, Wenxuan Liu, Jianhao Ding, Zhaofei Yu, Tiejun Huang
Spiking neural networks (SNNs) offer an energy-efficient computing paradigm through sparse event-driven computation, showing great potential for efficient multimodal learning. However, applying SNNs to high-level multimodal tasks, such as image-text retrieval (ITR), remains challenging, since sparse spike representations make it difficult to capture semantic structures required for cross-modal alignment. Existing spiking ITR methods rely on local alignment and additional soft-label supervision during training, while lacking awareness of structural and multi-granularity relationships. To address these issues, we propose a Multi-head Spiking Graph Attention Network (\textbf{MSGAT}) for structural modeling and equip it with dynamic attention heads to capture complementary relational patterns and enable spike-driven graph reasoning and aggregation. However, within a two-branch multi-granularity fusion framework, the fine-grained spike representations generated by MSGAT are sparse and discrete, whereas the global representations are continuous, making conventional feature-level fusion susceptible to interference across heterogeneous representations. Therefore, we introduce…
PDF arXiv
arXiv cs.AIOct 8
SpikeSSL: A Universal Spike Inference Framework with Dynamics-Informed State-Space Layers
Chenghao Yue, Siming Xing, Shuran Liu, Angran Li, Yuanlong Zhang
Two-photon calcium imaging is a standard tool for recording large neural populations in vivo, yet inferring spikes accurately across the growing diversity of calcium indicators remains an open problem. Existing supervised methods achieve reasonable in-domain accuracy but generalize poorly to unseen indicators, because different indicators induce distinct fluorescence kinetics and signal statistics while existing architectures remain relatively simple generic temporal regressors without dynamics-matched inductive bias. We propose SpikeSSL, a universal spike inference framework whose temporal backbone is a bank of bidirectional IIR state-space layers broadly motivated by calcium dynamics. A multi-modal conditioning encoder maps indicator identity, sampling rate, and trace-level signal statistics into a global conditioning vector that modulates the backbone via Adaptive Layer Normalization, while a heteroscedastic variance head provides calibrated per-frame uncertainty. On a benchmark with five fixed evaluation splits built from 33 public ground-truth datasets, SpikeSSL achieves state-of-the-art performance in both in-domain and zero-shot leave-one-indicator-out settings. We also…
PDF arXiv

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Activity · 12 weeks

Coming with more sources: repos

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Efficient spiking neural

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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

Named by an AI from these papers' abstracts.