Advanced Memory and Neural Computing

Latest papers

• SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

  Xiaoli Liu, Yujie Liang, Jialin Li, Malu Zhang · 2 October 2026

  Spiking Neural Networks (SNNs) enable event-driven computation through biologically inspired dynamics at the neuronal scale, while Mixture-of-Experts (MoE) perform conditional computation through expert selection at the model scale. Integrating their strengths offers potential for flexible neural ar…

• Contrastive Attention Mitigates Spectral Bias in Spiking Transformers

  Xiaoli Liu, Malu Zhang, Yang Yang · 2 October 2026

  Spiking Transformers merge the energy-efficiency of spiking neural networks (SNNs) with the representational power of self-attention, creating a promising architecture for high-performance, energy-efficient computation. However, a performance gap persists versus its counterparts in artificial neural…

• Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data

  Henok Teklu, Mustafa Sakhai, Matej Mertik, Maciej Wielgosz · 29 September 2026

  Event cameras are well suited to pedestrian crossing detection, and spiking neural networks (SNNs) can process their output natively, but current SNN detectors are trained with supervised backpropagation and therefore require costly frame-level crossing labels. We investigate crossing detection with…

• QuantaSpike: Short-Window Spike-Driven Quantization for Large Language Models

  Bang Hu, Guowei Zhu, Changze Lv, Xiaoqing Zheng, Fengzhe Zhang, Fan Zhang, Wei Cao · 29 September 2026

  Large language models (LLMs) achieve strong performance across many tasks but rely on dense multiply-accumulate (MAC) operations during inference, resulting in high energy cost. Spiking neural networks (SNNs) offer an event-driven alternative in which synaptic integration uses lightweight accumulati…

• The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation

  Soshun Kihara, Shunsuke Yasuki, Masato Taki · 28 September 2026

  Convolutional neural networks trained on ImageNet are known to exhibit a strong preference for local high-frequency texture, an inductive bias that translates into fragile robustness against distribution shifts in real-world environments. Event cameras, in contrast, record only changes in scene brig…

• On the second-order optimization for spiking neural networks

  Ngoc Phu Doan, Ihsen Alouani · 25 September 2026

  Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional neural networks by exploiting sparse, binary spikes, and event-driven computation. However, the training of SNNs remains challenging, as spiking activations create a sharp loss landscape that hinders training, and d…

• Modelling dynamic systems transfer functions from events in computational neuromorphic imaging

  Nimrod Kruger, Gregory Cohen · 25 September 2026

  Event Vision Sensing (EVS) report threshold crossings of log-irradiance, so a static optical system imaging a static scene produces no output at all. The classical procedure for measuring a Point Spread Function (PSF), illuminating the system with a constant point source, therefore has no event-base…

• Bend the Clock: Predicting Ahead to Beat Latency in Event-Based Object Detection

  Biswadeep Sen, Benoit R. Cottereau, Nicolas Cuperlier, Terence Sim · 24 September 2026

  Event cameras promise low-latency perception for high-speed robotic systems, where even short delays can render detections stale by the time they inform downstream robotic decisions. Yet modern event detectors still require tens of milliseconds of computation before their predictions become availabl…

• Activation-Energy Pruning for Spiking Neural Networks: Unsupervised Personalization via Spike-Count Saliency

  Joseph Bingham · 23 September 2026

  Activation-energy pruning -- removing weights whose product of magnitude and cumulative pre-synaptic spike count falls below a threshold -- was established as an effective unsupervised personalization strategy for conventional deep neural networks~\citep{BINGHAM2025101242}. This paper asks what happ…

• Can Spiking Neural Networks play pinball? A neuromorphic motion detector for target tracking

  Mazdak Fatahi, \v{S}\'arka Pryjmakov\'a, Pierre Boulet, Giulia D'Angelo · 22 September 2026

  Biological visual systems achieve continuous, low-latency motion perception by processing sparse, asynchronous spiking signals, enabling real-time tracking under strict energy constraints. Event-based cameras, inspired by the mammalian retina, replicate this efficiency by capturing only local bright…

• Event-Frame Fusion for Inter-Frame Segmentation via Event-Guided Motion

  Dalia Hareb, Jean Martinet, Benoit Miramond, Elisabetta Chicca · 22 September 2026

  Autonomous navigation requires precise and efficient semantic segmentation, yet existing frame-based approaches remain limited by motion blur, glare, latency, and the low temporal resolution (20-30 FPS) of conventional cameras, which leads to information loss between frames. Event cameras have emerg…

• On The Robustness-Resolution Tradeoff In Temporal Quantization Of Event Streams

  Sayeed Shafayet Chowdhury, Ruhi Sharmin · 22 September 2026

  Event pipelines often discretize asynchronous timestamps before learning. This step looks harmless, but its stability depends directly on temporal resolution. We study this dependence at the representation level. We first show that hard temporal binning is discontinuous: an arbitrarily small timesta…

• Predictive Suppression Layers for Communication-Efficient Spiking Neural Networks

  Aidin Attar, Michele Rossi · 21 September 2026

  Feedforward Spiking Neural Networks (SNNs) typically propagate every generated spike indiscriminately, disregarding whether the information is redundant from an information-theoretic perspective. This lack of selectivity induces high redundancy in inter-layer communication, creating an expensive ove…

• Field Tracking of Insects Using a Stereoscopic Event-Based Camera Setup

  Pratham G. Shenwai, Martin J. Lankheet, John T. Hrynuk, Mandiyam Y. Mahadeeswara, Mandyam V. Srinivasan, Sridhar Ravi · 21 September 2026

  High-speed tracking of small, fast-moving organisms in their natural environments is important to better understand their behavior and ecology. Traditional frame-based imaging suffers from motion blur due to low temporal resolution, and data storage limitations, propelling a search for more adaptive…

• Multi-viewpoint Geo-localization with Event Cameras

  Adam D. Hines, Michael Milford, Tobias Fischer · 21 September 2026

  Robot localization is an ongoing challenge that demands mapping and positioning systems that are tolerant to viewpoint change. Event cameras are attracting increasing interest and adoption in robotics; however, dealing with viewpoint variance is an under-investigated problem in existing event-based …

• Continuous Spiking Graph Neural Networks

  Shiqi Fan, Zeqing Zhang, Nan Yin, Tong Li, Hongyi Nie, Die Hu, Wen Hua · 21 September 2026

  Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, whi…

• NeuroFlex: Lossless Element-Level ANN-SNN Co-Execution for Efficient Sparse Inference

  Varun Manjunath, Pranav Ramesh, Gopalakrishnan Srinivasan · 18 September 2026

  Sparse DNN accelerators specialize in ANN or SNN execution, leaving energy or latency on the table when workload characteristics vary within a layer. Hybrid accelerator designs that switch modes at layer or tile granularity suffer from low PE utilization since one core type idles whenever the other …

• An Event Preserving Velocity Invariant Representation for Event Cameras

  Mikihiro Ikura, Luna Gava, Jiahang Wu, Chiara Bartolozzi, Arren Glover · 18 September 2026

  Event cameras provide low-latency, high temporal resolution perception for real-time vision tasks such as robotics.The novel circuitry (i.e. asynchronous, independent pixels) that enables these advantages also introduces new algorithmic challenges. Velocity-invariant representations alleviate missin…

• PointEvent: Rethinking Event-based Tiny Object Detection via Serialized Motion Evidence Accumulation

  Zongze Wu, Baofeng Jia, Weiqi Yan, Jingyuan Zhang, Yu Zang, Xiaoyu Chen, Jing Han · 18 September 2026

  Event cameras offer high temporal resolution and motion sensitivity for tiny UAV detection, yet distant targets generate sparse and fragmented events that are easily overwhelmed by clutter and ego-motion. Existing methods mainly rely on dense event representations or local sparse spatiotemporal mode…

• REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception

  Geoffroy Keime, Nicolas Cuperlier, Benoit R. Cottereau · 18 September 2026

  Robotic systems operating in dynamic environments require visual perception that evolves continuously with the incoming sensory stream. Event cameras provide microsecond temporal resolution and asynchronous sensing, but most learning-based methods accumulate events into frames or temporal bins, intr…

• BLADE: ReliaBle Dynamic Hardware-Aware SNN-ANN Boundary SeLection for Event-BAseD Object DEtection

  Mahdi Taheri, Alwin Paul · 17 September 2026

  Hybrid Spiking Neural Network (SNN)-Artificial Neural Network (ANN) architectures combine the energy efficiency of SNNs with the superior detection accuracy of ANNs for event-based object detection. Existing hybrid SNN--ANN networks, however, employ static inference and select the SNN-ANN boundary p…

• Event-based Selective Attention for Multi-resolution Fast Region of Interest (ROI) Detection

  Luca Peres, Giulia D'Angelo, Chiara Bartolozzi, Oliver Rhodes · 16 September 2026

  Neuromorphic vision systems operate under strict constraints on bandwidth, memory, and energy, particularly at the edge, motivating early mechanisms for data reduction and selective processing. In this work, we investigate a multi-scale training-free, saliency-based, bottom-up visual attention model…

• Hyper-RED: Scalable Event Pre-training via Semantic Hypergraph Distillation

  Meisen Wang, Zhiqiang Tian, Wei Bao, Chengjie Wang, Shaoyi Du, Siqi Li · 16 September 2026

  Event cameras have shown great potential for robust visual perception, yet scaling event representation learning remains challenging due to the scarcity of large-scale annotated event data. Pretrained image models provide scalable semantic supervision, but existing image-to-event methods rely on rig…

• Syn2Logic: End-to-End Neuromorphic Design Automation

  Artur Podobas · 16 September 2026

  In this work, we propose a view on electronic Neuromorphic Design Automation (eNDA), which we see as a design automation flow that bridges computational neuroscience modeling with traditional Electronic Design Automation (EDA) flow. We introduce the term, give examples of how it can be implemented, …

• Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams

  Dalton Diez, Peyton Andras, Max Shroyer, James Ghawaly Jr · 16 September 2026

  Spiking neural networks (SNNs) have shown promise for sparse, event-driven computation through stateful processing that is naturally compatible with low-power edge hardware. These properties align with cyber monitoring, where data arrives asynchronously, and malicious behavior often emerges through …