Neuromorphic Computing

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Neuromorphic computing studies algorithms and physical systems that compute through neuron-like dynamics, spikes, sparse events, and sometimes sensing integrated with processing. Common approaches include spiking neural networks trained with surrogate gradients or converted from conventional networks, local learning such as spike-timing-dependent plasticity, reservoir computing, and specialized CMOS, FPGA, memristor, photonic, or analog hardware. These systems are tested on event-camera vision, speech, time-series, robotics, and edge sensing, with benchmarks such as DVS Gesture, N-MNIST, SHD, and CIFAR variants. The central challenge is turning sparse activity and device-level efficiency into reliable end-to-end gains without sacrificing accuracy, scalability, or fair comparison with conventional hardware.

Latest papers

192 papers15 papers in the last four weeks, down 25% on the four weeks before. 0.1% of all new papers.

ITP-STDP: A Hardware-Efficient Intrinsic-Timing Power-of-Two Synaptic Learning Engine for On-Chip SNNs
Jun 4, 2026Haihang Xia, Xinyu Zhao, Xuecheng Wang +5Spiking Neural Networks Spike-Timing-Dependent Plasticity
School of Electrical and Electronic Engineering, The University of Shefﬁeld, S1 3JD Shefﬁeld, U.K. · Donghai Laboratory, Zhoushan 316021, China · Engineering Research Center of Oceanic Sensing Technologyand Equipment, Ministry of Education, Zhoushan 316021, China +1
Signed Spiking Neuron Enabled by an Orthogonal-Easy-Axis Magnetic Tunnel Junction
Jun 2, 2026Huannan Zheng, Jingli Liu, Kezhou YangLIF Neurons Spiking Neural Networks
Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 51000, China
Spiking and Event-driven Neuromorphic Mamba Models for Efficient Speech Recognition
May 31, 2026Tauseef Ahmed, Tao Sun, Jeronimo Castrillon +2Efficient Neural Network Inference Mamba
Department of Advanced Computing Sciences, Maastricht University, Netherlands · Hardware-Efficient AI Team, IMEC, Netherlands · Chair for Compiler Construction, TU Dresden, Germany +1
Memristor-Based Spiking Neural Network Accelerator for Bio-inspired Interception Task
May 29, 2026Qianhou Qu, Sheng Lu, Liuting Shang +4Efficient Neural Network Inference Compute-in-Memory
Department of Electrical Engineering The University of Texas at Arlington Arlington, USA · Department of Electrical Engineering and Computer Science South Dakota State University Brookings, USA
CLANE: Continual Learning of Actions on Neuromorphic Hardware from Event Cameras
May 27, 2026Elvin Hajizada, Michael Neumeier, Edward Paxon Frady +4Event-Based Vision Continual Learning
Institute of Informatics, University of Munich (LMU), Munich, Germany · fortiss GmbH, Neuromorphic Computing, Munich, Germany · Technical University of Munich, TUM School of CIT, Munich, Germany +5
Neuromorphic LiDAR-based Bird's Eye View Object Detection using Energy-efficient Spiking Neural Networks
May 24, 2026Sambit Mohapatra, Senthil Yogamani, Heinrich Gotzig +1Lidar-Based 3D Object Detection Autonomous Driving Perception
Valeo, Germany · TU Ilmenau, Germany · Valeo, Ireland
UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy
May 22, 2026Qinghui Xing, Zhuo Chen, Xin Du +6Spiking Neural Networks Neuromorphic Computing
Zhejiang University, Hangzhou, China
SpikingMoE: SDPrompt-Guided Dynamic Expert Fusion in Spiking Neural Networks
May 22, 2026Yukai Yang, Chenxi Qin, Jungang Li +3Spiking Transformers Dynamic Neural Networks
Tianjin University, Medical School, Tianjin 300072, China · Southern University of Science and Technology, Department of Computer Science and Engineering, Shenzhen, Guangdong 518055, China · Chinese Academy of Medical Sciences, Institute of Biomedical Engineering, Tianjin 300192, China
ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing
May 20, 2026Kang You, Chen Nie, Lee Jun Yan +6Efficient Neural Network Inference Spiking Neural Networks
Intelligent Computing Research Group, School of Computer Science, Shanghai Jiao Tong University, Shanghai, CN · Shanghai AI Laboratory, Shanghai, CN · School of Computer Science, Shanghai Jiao Tong University, Shanghai, CN +1
Reconfigurable Nonlinear Photonic Networks for In-Situ Learning and Memory Formation via Driven-Dissipative Dynamics
May 19, 2026Isaac YorkeDynamical Systems Neural Network Memorization
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A I-43124 Parma, Italy
Plug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers
May 19, 2026Xinzhe Yuan, Xiang Peng, Bin Gu +1Spiking Transformers Spiking Neural Networks
IASM, Harbin Institute of Technology · IASM, Harbin Institute of Technology, China · School of Artificial Intelligence, Jilin University +1
Neuromorphic Control of a Flapping-Wing Robot on Resource-Constrained Hardware
May 19, 2026Rim El Filali, Chenrui Feng, Chao Gao +1Efficient Neural Network Inference Aerial Robotics
Institute for AI Industry Research (AIR), Tsinghua University, Beijing, PR China · Department of Computer Science and Technology, Tsinghua University, Beijing, PR China · Xinchen Qihang Inc., Beijing, PR China
Scalable neuromorphic computing from autonomous spiking dynamics in a clockless reconfigurable chip
May 15, 2026Eric Oliveira Gomes, Damien RontaniEnergy-Efficient ML Spiking Neural Networks
LMOPS UR4423 Laboratory, CentraleSup´elec and Universit´e de Lorraine, Metz F-57070, France
Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
May 13, 2026Johnson Zhou, Daniel Tanneberg, Forough Habibollahi +12Neuromorphic Computing Neural Encoding
Cortical Labs, Australia · Honda Research Institute Europe, Germany
Heterogeneous SoC Integrating an Open-Source Recurrent SNN Accelerator for Neuromorphic Edge Computing on FPGA
May 12, 2026Michelangelo Barocci, Vittorio Fra, Enrico Macii +1Edge Computing Heterogeneous Computing
Department of Computer and Control Engineering, Politecnico di Torino, Turin, Italy · Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, Italy
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
May 12, 2026Arthur Fyon, Julien Brandoit, Loris Mendolia +3Energy-Efficient ML Keyword Spotting
University of Liège Liège, Belgium
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
May 12, 2026Julien Brandoit, Arthur Fyon, Damien Ernst +1Energy-Efficient ML Recurrent Neural Networks
Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
Multi-Timescale Conductance Spiking Networks: A Sparse, Gradient-Trainable Framework with Rich Firing Dynamics for Enhanced Temporal Processing
May 12, 2026Alex Fulleda-Garcia, Saray Soldado-Magraner, Josep Maria Margarit-TauléSpiking Neural Networks Neuromorphic Computing
Instituto de Microelectrónica de Barcelona (IMB-CNM), Consejo Superior de Investigaciones Cientíﬁcas (CSIC) Cerdanyola del Vallès, Spain · Department of Neurobiology, University of California Los Angeles (UCLA) Los Angeles, USA · Joint Senior authors
Leveraging Non-Equilibrium ECRAM Dynamics for Short-Term Plasticity in Neuromorphic Circuits
May 11, 2026Alex Currie, Sean Borkholder, Nithil Harris Manimaran +4LIF Neurons Spiking Neural Networks
RAMLab, Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester NY, USA · Iontronic and Nanoelectronics Lab, School of Physics and Astronomy, Rochester Institute of Technology, Rochester NY, USA · Brain Lab, Department of Computer Engineering, Rochester Institute of Technology, Rochester NY, USA
Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopes
May 11, 2026Yasmine Abu-Haeyeh, Tobias Ladner, Matthias Althoff +1Formal Hardware Verification Neural Network Verification
Goethe University Frankfurt, Germany · Technical University of Munich, Germany
Neuromorphic Satellite Intelligence: A Formal Framework for Event-Driven Architectures, Spike-Based Algorithms, and Quantitative Energy-Accuracy Trade-offs
May 11, 2026Suresh Kumar TP, Vikas AgnihotriNeuromorphic Computing Spiking Neural Networks
Department of Electrical and Electronics Engineering, Mangalayatan University, India · Independent Researcher, India
End-to-End Keyword Spotting on FPGA Using Graph Neural Networks with a Neuromorphic Auditory Sensor
May 10, 2026Wiktor Matykiewicz, Piotr Wzorek, Kamil Jeziorek +4Keyword Spotting Speech Processing
AGH University of Krakow, Poland · Embedded Vision Systems Group, Computer Vision Laboratory · Robotics and Technology of Computers Lab., ETSII, EPS, SCORE, I3US, Universidad de Sevilla
Uncertainty-Aware Token Importance Estimation in Spiking Transformers
May 10, 2026Wenxuan Liu, Zecheng Hao, Tong Bu +2Spiking Transformers Uncertainty Quantification
School of Computer Science, Peking University · School of Computer Science, Peking University. · Institute for Artificial Intelligence, Peking University +2
Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects
May 8, 2026Hudi He, Fukun Wang, Zhe Wang +7Benchmark Design Algorithmic Fairness
Jilin University · Nanyang Technological University · RMIT University
Neuromorphic visual attention for Sign-language recognition on SpiNNaker
May 7, 2026Sarka Liskova, Olha Vedmedenko, Mazdak Fatahi +3Event-Based Vision Visual Attention
Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic · Faculty of Information Technology, Czech Technical University in Prague, Czech Republic
Efficient event-driven retrieval in high-capacity kernel Hopfield networks
May 7, 2026Akira TamamoriHopfield Networks Neuromorphic Computing
Faculty of Information Science, Aichi Institute of Technology 1247 Yachigusa, Yakusa-cho, Toyota-shi, Aichi 470-0392, Japan
Bio-plausible Neuromorphic Disturbance Observer Based on Emulation Theory: Extended Version
May 5, 2026Hongfu Xu, Xiaoyu Guo, Shengbo Wang +1Feedback Control Adaptive Control
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China · Department of Mechanical Engineering, City University of Hong Kong, Hong Kong · Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong +1
Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation
May 1, 2026Bo Tang, Weiwei XieRecurrent Neural Networks Spiking Neural Networks
Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester, USA
Real-Time Frame- and Event-based Object Detection with Spiking Neural Networks on Edge Neuromorphic Hardware: Design, Deployment and Benchmark
Apr 30, 2026Udayanga G. W. K. N. Gamage, Yan Zeng, Cesar Cadena +2Edge Inference Spiking Neural Networks
Department of Electrical and Photonics Engineering, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark · Faculty of Engineering, University of Ruhuna, Hapugala,Galle 80000, Sri Lanka · Autonomous Systems Lab, ETH zürich, Leonhardstrasse 21, 8092 Zürich, Switzerland
NeuroRing: Scaling Spiking Neural Networks via Multi-FPGA Bidirectional Ring Topologies and Stream-Dataflow Architectures
Apr 30, 2026Muhammad Ihsan Al Hafiz, Artur PodobasSpiking Neural Networks Neuromorphic Computing
KTH Royal Institute of Technology, Stockholm, Sweden

Jun 4, 2026J/K move · Enter open · S save

ITP-STDP: A Hardware-Efficient Intrinsic-Timing Power-of-Two Synaptic Learning Engine for On-Chip SNNs

Haihang Xia, Xinyu Zhao, Xuecheng Wang, John Goodenough+4

School of Electrical and Electronic Engineering, The University of Shefﬁeld, S1 3JD Shefﬁeld, U.K. · Donghai Laboratory, Zhoushan 316021, China · Engineering Research Center of Oceanic Sensing Technologyand Equipment, Ministry of Education, Zhoushan 316021, China+1

Spiking Neural Networks Spike-Timing-Dependent Plasticity Neuromorphic Computing FPGA-Based Neural Network Acceleration

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Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attention across a wide range of applications. However, the large number of synaptic connections in SNNs leads to intensive weight-update computation by on-chip learning algorithms during training, resulting in substantial hardware resource utilization and energy consumption. Among existing SNN learning algorithms, spike-timing-dependent plasticity (STDP) is one of the most extensively studied and widely adopted, serving as a fundamental learning component in SNNs. To address the hardware and energy overheads associated with SNN training, this paper presents intrinsic-timing power-of-two STDP (ITP-STDP) and its corresponding prototype learning engine hardware architecture. The proposed design is evaluated through a dedicated mean-field synaptic drift model for dynamical analysis and further validated across SNN networks of different scales and datasets. It is further implemented on both ASIC and FPGA platforms and compared with state-of-the-art approaches, including the original STDP and more complex STDP variants. The results demonstrate superior energy efficiency, higher operating speed, and substantially lower hardware resource utilization, as the proposed design eliminates most of the computational overhead of STDP through both algorithmic and hardware-level optimizations. On the FPGA platform, the proposed design improves energy efficiency by 4.5

\times

to 219.8

\times

over the compared designs. On the ASIC platform, the proposed design achieves a 4.8

\times

to 22.01

\times

speedup while consuming only 1.2% to 3.3% of the area required by prior works.