Neuromorphic Computing

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Overview

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

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

Apr 1, 2026

Integer-State Dynamics of Quantized Spiking Neural Networks for Efficient Hardware Acceleration

Spiking neural networks (SNNs) support energy-efficient machine intelligence because event-driven computation and sparse activity map naturally to low-power digital hardware. In practical implementations, however, membrane states, synaptic weights, and thresholds are represented with finite-precision integer arithmetic. Quantization, clipping, and overflow can therefore alter network dynamics, not just approximate a higher-precision model. This paper adopts an integer-state dynamical perspective, modeling a hardware-oriented SNN as a deterministic map on a bounded integer lattice. Under this view, recurrence, periodic orbits, and regime changes become intrinsic properties of the system. We introduce a lightweight update rule with integer-valued states and shift-based leakage, and demonstrate the approach through exploratory simulations with network sizes N = 30-130, connection densities 0.1-0.9, and bit widths 4/8/16 over T = 1000 steps. The results show bounded and recurrent temporal structure with strong quantization sensitivity. The observed regimes depend heavily on representation semantics and scaling choices. These findings suggest that numerical precision acts as a dynamical design variable and highlight integer-state analysis as a useful framework for hardware-aware SNN co-design, motivating future work on attractor analysis, precision-aware training, and FPGA/ASIC validation.

Lei Zhang

Faculty of Engineering and Applied Science, University of Regina

Spiking Neural NetworksNeuromorphic ComputingNeural Network QuantizationDynamical Systems
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Mar 30, 2026

AceleradorSNN: A Neuromorphic Cognitive System Integrating Spiking Neural Networks and DynamicImage Signal Processing on FPGA

The demand for high-speed, low-latency, and energy-efficient object detection in autonomous systems -- such as advanced driver-assistance systems (ADAS), unmanned aerial vehicles (UAVs), and Industry 4.0 robotics -- has exposed the limitations of traditional Convolutional Neural Networks (CNNs). To address these challenges, we have developed AceleradorSNN, a third-generation artificial intelligence cognitive system. This architecture integrates a Neuromorphic Processing Unit (NPU) based on Spiking Neural Networks (SNNs) to process asynchronous data from Dynamic Vision Sensors (DVS), alongside a dynamically reconfigurable Cognitive Image Signal Processor (ISP) for RGB cameras. This paper details the hardware-oriented design of both IP cores, the evaluation of surrogate-gradienttrained SNN backbones, and the real-time streaming ISP architecture implemented on Field-Programmable Gate Arrays (FPGA).

Daniel Gutierrez, Ruben Martinez, Leyre Arnedo +2

Intigia R&D Department, Alicante, Spain

Neuromorphic ComputingFPGA-Based Neural Network AccelerationSpiking Neural NetworksEvent-Based Vision
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Mar 29, 2026

The role of neuromorphic principles in the future of biomedicine and healthcare

Neuromorphic engineering has matured over the past four decades and is currently experiencing explosive growth with the potential to transform biomedical engineering and neurotechnologies. Participants at the Neuromorphic Principles in Biomedicine and Healthcare (NPBH) Workshop (October 2024) -- representing a broad cross-section of the community, including early-career and established scholars, engineers, scientists, clinicians, industry, and funders -- convened to discuss the state of the field, current and future challenges, and strategies for advancing neuromorphic research and development for biomedical applications. Publicly approved recordings with transcripts (https://2024.neuro-med.org/program/session-video-and-transcripts) and slides (https://2024.neuro-med.org/program/session-slides) can be found at the workshop website.

Grace M. Hwang, Jessica D. Falcone, Joseph D. Monaco +9

National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), The Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative, Bethesda, MD 20892, USA · National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH, Bethesda, MD 20892, USA · National Institute of Mental Health (NIMH), NIH, Bethesda, MD 20892, USA +4

Neuromorphic Computing
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Mar 29, 2026

An Energy-Efficient Spiking Neural Network Architecture for Predictive Insulin Delivery

Diabetes mellitus affects over 537 million adults worldwide. Insulin-dependent patients require continuous glucose monitoring and precise dose calculation while operating under strict power budgets on wearable devices. This paper presents PDDS - an in-silico, software-complete research prototype of an event-driven computational pipeline for predictive insulin dose calculation. Motivated by neuromorphic computing principles for ultra-low-power wearable edge devices, the core contribution is a three-layer Leaky Integrate-and-Fire (LIF) Spiking Neural Network trained on 128,025 windows from OhioT1DM (66.5% real patients) and the FDA-accepted UVa/Padova physiological simulator (33.5%), achieving 85.90% validation accuracy. We present three rigorously honest evaluations: (1) a standard test-set comparison against ADA threshold rules, bidirectional LSTM (99.06% accuracy), and MLP (99.00%), where the SNN achieves 85.24% - we demonstrate this gap reflects the stochastic encoding trade-off, not architectural failure; (2) a temporal benchmark on 426 non-obvious clinician-annotated hypoglycemia windows where neither the SNN (9.2% recall) nor the ADA rule (16.7% recall) performs adequately, identifying the system's key limitation and the primary direction for future work; (3) a power-efficiency analysis showing the SNN requires 79,267x less energy per inference than the LSTM (1,551 Femtojoules vs. 122.9 nanojoules), justifying the SNN architecture for continuous wearable deployment. The system is not yet connected to physical hardware; it constitutes the computational middle layer of a five phase roadmap toward clinical validation. Keywords: spiking neural network, glucose severity classification, edge computing, hypoglycemia detection, event-driven architecture, LIF neuron, Poisson encoding, OhioT1DM, in-silico, neuromorphic, power efficiency.

Sahil Shrivastava

Predictive Drug Delivery System (PDDS) Project, Independent Research

Spiking Neural NetworksEfficient Neural Network InferenceNeuromorphic Computing
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Mar 25, 2026

Reconstructing Spiking Neural Networks Using a Single Neuron with Autapses

Spiking neural networks (SNNs) are promising for neuromorphic computing, but high-performing models still rely on dense multilayer architectures with substantial communication and state-storage costs. Inspired by autapses, we propose time-delayed autapse SNN (TDA-SNN), a framework that reconstructs SNNs with a single leaky integrate-and-fire neuron and a prototype-learning-based training strategy. By reorganizing internal temporal states, TDA-SNN can realize reservoir, multilayer perceptron, and convolution-like spiking architectures within a unified framework. Experiments on sequential, event-based, and image benchmarks show competitive performance in reservoir and MLP settings, while convolutional results reveal a clear space--time trade-off. Compared with standard SNNs, TDA-SNN greatly reduces neuron count and state memory while increasing per-neuron information capacity, at the cost of additional temporal latency in extreme single-neuron settings. These findings highlight the potential of temporally multiplexed single-neuron models as compact computational units for brain-inspired computing.

Wuque Cai, Hongze Sun, Quan Tang +6

Brain-Apparatus Communication Institute, University of Electronic Science and Technology of China, Chengdu, China · School of Artificial Intelligence, Chongqing University of Education, Chongqing, China

Spiking Neural NetworksNeuromorphic ComputingReservoir Computing
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Mar 25, 2026

Bridging Biological Hearing and Neuromorphic Computing: End-to-End Time-Domain Audio Signal Processing with Reservoir Computing

Despite the advancements in cutting-edge technologies, audio signal processing continues to pose challenges and lacks the precision of a human speech processing system. To address these challenges, we propose a novel approach to simplify audio signal processing by leveraging time-domain techniques and reservoir computing. Through our research, we have developed a real-time audio signal processing system by simplifying audio signal processing through the utilization of reservoir computers, which are significantly easier to train. Feature extraction is a fundamental step in speech signal processing, with Mel Frequency Cepstral Coefficients (MFCCs) being a dominant choice due to their perceptual relevance to human hearing. However, conventional MFCC extraction relies on computationally intensive time-frequency transformations, limiting efficiency in real-time applications. To address this, we propose a novel approach that leverages reservoir computing to streamline MFCC extraction. By replacing traditional frequency-domain conversions with convolution operations, we eliminate the need for complex transformations while maintaining feature discriminability. We present an end-to-end audio processing framework that integrates this method, demonstrating its potential for efficient and real-time speech analysis. Our results contribute to the advancement of energy-efficient audio processing technologies, enabling seamless deployment in embedded systems and voice-driven applications. This work bridges the gap between biologically inspired feature extraction and modern neuromorphic computing, offering a scalable solution for next-generation speech recognition systems.

Rinku Sebastian, Simon O'Keefe, Martin Trefzer

University of York. York, United Kingdom.

Speech ProcessingReservoir ComputingNeuromorphic Computing
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Mar 25, 2026

Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks

On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, recent studies show that feedback-control optimizers can enable expressive, on-chip training of neuromorphic devices. In this work, we present a proof-of-concept implementation of such feedback-control optimizers on a mixed-signal neuromorphic processor. We assess the proposed approach in an In-The-Loop(ITL) training setup on both a binary classification task and the nonlinear Yin-Yang problem, demonstrating on-chip training that matches the performance of numerical simulations and gradient-based baselines. Our results highlight the feasibility of feedback-driven, online learning under realistic mixed-signal constraints, and represent a co-design approach toward embedding such rules directly in silicon for autonomous and adaptive neuromorphic computing.

Jonathan Haag, Christian Metzner, Dmitrii Zendrikov +4

Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zürich, Switzerland · AI Center, ETH, Zürich, Switzerland · Digital Society Initiative, University of Zurich, Switzerland

On-Device TrainingNeuromorphic ComputingSpiking Neural Networks
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Mar 25, 2026

Machine vision with small numbers of detected photons per inference

Machine vision, including object recognition and image reconstruction, is a central technology in many consumer devices and scientific instruments. The design of machine-vision systems has been revolutionized by the adoption of end-to-end optimization, in which the optical front end and the post-processing back end are jointly optimized. However, while machine vision currently works extremely well in moderate-light or bright-light situations -- where a camera may detect thousands of photons per pixel and billions of photons per frame -- it is far more challenging in very low-light situations. We introduce photon-aware neuromorphic sensing (PANS), an approach for end-to-end optimization in highly photon-starved scenarios. The training incorporates knowledge of the low photon budget and the stochastic nature of light detection when the average number of photons per pixel is near or less than 1. We report a proof-of-principle experimental demonstration in which we performed low-light image classification using PANS, achieving 73% (82%) accuracy on FashionMNIST with an average of only 4.9 (17) detected photons in total per inference, and 86% (97%) on MNIST with 8.6 (29) detected photons -- orders of magnitude more photon-efficient than conventional approaches. We also report simulation studies showing how PANS could be applied to other classification, event-detection, and image-reconstruction tasks. By taking into account the statistics of measurement results for non-classical states or alternative sensing hardware, PANS could in principle be adapted to enable high-accuracy results in quantum and other photon-starved setups.

Shi-Yuan Ma, Jérémie Laydevant, Mandar M. Sohoni +3

School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA · Present address: Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. · NTT Physics and Informatics Laboratories, NTT Research, Inc., Sunnyvale, CA 94085, USA +2

Image ClassificationHardware-Software Co-DesignNeuromorphic Computing
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Mar 24, 2026

Latency Coding for Efficient and Low-Latency Deep Spiking Neural Networks

Spiking neural networks (SNNs) offer a biologically inspired computing paradigm with significant potential for energy-efficient neural processing. Among neural coding schemes of SNNs, Time-To-First-Spike (TTFS) coding, which encodes information through the precise timing of a neuron's first spike, provides exceptional activity sparsity and energy efficiency. However, existing TTFS models lack efficient training methods, suffering from high inference latency and limited performance, limiting their practicality on neuromorphic hardware. In this work, we propose latency coding, an extension of TTFS coding, and present a compatible framework that enables the efficient training of deep latency-coded SNNs by leveraging backpropagation through time (BPTT) algorithm. The framework includes: (1) a latency encoding (LE) module with feature extraction and straight-through estimators to address severe information loss in direct intensity-to-latency mapping; (2) relaxation of the strict single-spike constraint in intermediate layers to improve information propagation and gradient flow; and (3) a temporal adaptive decision (TAD) loss function that dynamically weights supervision signals based on the model's confidence, balancing the trade-off between speed and accuracy. Experimental results demonstrate that our method achieves competitive or superior accuracy compared with existing TTFS-coded SNNs with ultra-low inference latency and high energy efficiency. Latency-coded SNNs also demonstrate improved robustness against input perturbations. These findings highlight latency coding as a practical and hardware-friendly approach for fast and energy-efficient neuromorphic processing.

Yi Lu, Jianhao Ding, Zhaofei Yu

School of Software and Microelectronics, Peking University, Beijing, China · School of Computer Science, Peking University, Beijing, China · Institute for Artificial Intelligence and the School of Computer Science, Peking University, Beijing, China

Efficient Neural Network InferenceSpiking Neural NetworksNeuromorphic Computing
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Mar 24, 2026

Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition

Reliable visual place recognition (VPR) under dynamic real-world conditions is critical for autonomous robots, yet conventional deep networks remain limited by high computational and energy demands. Inspired by the mammalian navigation system, we introduce SpikeVPR, a bio-inspired and neuromorphic approach combining event-based cameras with spiking neural networks (SNNs) to generate compact, invariant place descriptors from few exemplars, achieving robust recognition under extreme changes in illumination, viewpoint, and appearance. SpikeVPR is trained end-to-end using surrogate gradient learning and incorporates EventDilation, a novel augmentation strategy enhancing robustness to speed and temporal variations. Evaluated on two challenging benchmarks (Brisbane-Event-VPR and NSAVP), SpikeVPR achieves performance comparable to state-of-the-art deep networks while using 50 times fewer parameters and consuming 30 and 250 times less energy, enabling real-time deployment on mobile and neuromorphic platforms. These results demonstrate that spike-based coding offers an efficient pathway toward robust VPR in complex, changing environments.

Geoffroy Keime, Nicolas Cuperlier, Benoit R. Cottereau

IPAL, CNRS IRL 2955, Singapore · Univ Toulouse, CNRS, CerCo UMR 5549, Toulouse, France · Laboratoire ETIS UMR 8051, CY Cergy-Paris Université, ENSEA, CNRS, Cergy, France

Visual Place RecognitionNeuromorphic ComputingSpiking Neural NetworksEfficient Neural Network InferenceEvent-Based Vision
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Mar 19, 2026

Brain-inspired AI for Edge Intelligence: a systematic review

While Spiking Neural Networks (SNNs) promise to circumvent the severe Size, Weight, and Power (SWaP) constraints of edge intelligence, the field currently faces a "Deployment Paradox" where theoretical energy gains are frequently negated by the inefficiencies of mapping asynchronous, event-driven dynamics onto traditional von Neumann substrates. Transcending the reductionism of algorithm-only reviews, this survey adopts a rigorous system-level hardware-software co-design perspective to examine the 2020-2025 trajectory, specifically targeting the "last mile" technologies - from quantization methodologies to hybrid architectures - that translate biological plausibility into silicon reality. We critically dissect the interplay between training complexity (the dichotomy of direct learning vs. conversion), the "memory wall" bottlenecking stateful neuronal updates, and the critical software gap in neuromorphic compilation toolchains. Finally, we envision a roadmap to reconcile the fundamental "Sync-Async Mismatch," proposing the development of a standardized Neuromorphic OS as the foundational layer for realizing a ubiquitous, energy-autonomous Green Cognitive Substrate.

Yingchao Cheng, Meijia Wang, Zhifeng Hao +1

Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, 518107, China · Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia · College of Mathematics and Computer Science, Shantou University, Shantou, 515063, China

Neuromorphic ComputingSpiking Neural NetworksEnergy-Efficient MLEdge ML
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Mar 18, 2026

A Synthesizable RTL Implementation of Predictive Coding Networks

Backpropagation has enabled modern deep learning but is difficult to realize as an online, fully distributed hardware learning system due to global error propagation, phase separation, and heavy reliance on centralized memory. Predictive coding offers an alternative in which inference and learning arise from local prediction-error dynamics between adjacent layers. This paper presents a digital architecture that implements a discrete-time predictive coding update directly in hardware. Each neural core maintains its own activity, prediction error, and synaptic weights, and communicates only with adjacent layers through hardwired connections. Supervised learning and inference are supported via a uniform per-neuron clamping primitive that enforces boundary conditions while leaving the internal update schedule unchanged. The design is a deterministic, synthesizable RTL substrate built around a sequential MAC datapath and a fixed finite-state schedule. Rather than executing a task-specific instruction sequence inside the learning substrate, the system evolves under fixed local update rules, with task structure imposed through connectivity, parameters, and boundary conditions. The contribution of this work is not a new learning rule, but a complete synthesizable digital substrate that executes predictive-coding learning dynamics directly in hardware.

Timothy Oh

University of California, Riverside

Neural Network Hardware AccelerationPredictive CodingNeuromorphic ComputingOnline Learning
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Mar 17, 2026

Linearized Bregman Iterations for Sparse Spiking Neural Networks

Spiking Neural Networks (SNNs) offer an energy efficient alternative to conventional Artificial Neural Networks (ANNs) but typically still require a large number of parameters. This work introduces Linearized Bregman Iterations (LBI) as an optimizer for training SNNs, enforcing sparsity through iterative minimization of the Bregman distance and proximal soft thresholding updates. To improve convergence and generalization, we employ the AdaBreg optimizer, a momentum and bias corrected Bregman variant of Adam. Experiments on three established neuromorphic benchmarks, i.e. the Spiking Heidelberg Digits (SHD), the Spiking Speech Commands (SSC), and the Permuted Sequential MNIST (PSMNIST) datasets, show that LBI based optimization reduces the number of active parameters by about 50% while maintaining accuracy comparable to models trained with the Adam optimizer, demonstrating the potential of convex sparsity inducing methods for efficient neuromorphic learning.

Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr

Silicon Austria Labs, Linz, Austria · Software Competence Center Hagenberg, Hagenberg, Austria · Institute of Signal Processing, Johannes Kepler University, Linz, Austria

Spiking Neural NetworksSparse Neural NetworksNeural Network OptimizationNeuromorphic Computing
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Mar 16, 2026

Determinism in the Undetermined: Deterministic Output in Charge-Conserving Continuous-Time Neuromorphic Systems with Temporal Stochasticity

Achieving deterministic computation results in asynchronous neuromorphic systems remains a fundamental challenge due to the inherent temporal stochasticity of continuous-time hardware. To address this, we develop a unified continuous-time framework for spiking neural networks (SNNs) that couples the Law of Charge Conservation with minimal neuron-level constraints. This integration ensures that the terminal state depends solely on the aggregate input charge, providing a unique cumulated output invariant to temporal stochasticity. We prove that this mapping is strictly invariant to spike timing in acyclic networks, whereas recurrent connectivity can introduce temporal sensitivity. Furthermore, we establish an exact representational correspondence between these charge-conserving SNNs and quantized artificial neural networks, bridging the gap between static deep learning and event-driven dynamics without approximation errors. These results establish a rigorous theoretical basis for designing continuous-time neuromorphic systems that harness the efficiency of asynchronous processing while maintaining algorithmic determinism.

Jing Yan, Kang You, Zhezhi He +1

School of Mathematical Sciences, Institute of Natural Sciences and MOE-LSC, Shanghai Jiao Tong University, Shanghai, China · School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China

Neuromorphic ComputingSpiking Neural Networks
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Mar 16, 2026

S2Act: Simple Spiking Actor

Spiking neural networks (SNNs) and biologically-inspired learning mechanisms are attractive in mobile robotics, where the size and performance of onboard neural network policies are constrained by power and computational budgets. Existing SNN approaches, such as population coding, reward modulation, and hybrid artificial neural network (ANN)-SNN architectures, have shown promising results; however, they face challenges in complex, highly stochastic environments due to SNN sensitivity to hyperparameters and inconsistent gradient signals. To address these challenges, we propose simple spiking actor (S2Act), a computationally lightweight framework that deploys an RL policy using an SNN in three steps: (1) architect an actor-critic model based on an approximated network of rate-based spiking neurons, (2) train the network with gradients using compatible activation functions, and (3) transfer the trained weights into physical parameters of rate-based leaky integrate-and-fire (LIF) neurons for inference and deployment. By globally shaping LIF neuron parameters such that their rate-based responses approximate ReLU activations, S2Act effectively mitigates the vanishing gradient problem, while pre-constraining LIF response curves reduces reliance on complex SNN-specific hyperparameter tuning. We demonstrate our method in two multi-agent stochastic environments (capture-the-flag and parking) that capture the complexity of multi-robot interactions, and deploy our trained policies on physical TurtleBot platforms using Intel's Loihi neuromorphic hardware. Our experimental results show that S2Act outperforms relevant baselines in task performance and real-time inference in nearly all considered scenarios, highlighting its potential for rapid prototyping and efficient real-world deployment of SNN-based RL policies.

Ugur Akcal, Seung Hyun Kim, Mikihisa Yuasa +6

Aerospace Engineering · Computer Science · Coordinated Science Laboratory +4

Spiking Neural NetworksRobotic RLReinforcement LearningNeuromorphic Computing
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Mar 14, 2026

Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics

Deploying trustworthy artificial intelligence on edge robotics imposes a difficult trade-off between high-assurance robustness and energy sustainability. Traditional defense mechanisms against adversarial attacks typically incur significant computational overhead, threatening the viability of power-constrained platforms in environments such as cislunar space. This paper quantifies the energy cost of assurance in event-driven neuromorphic systems. We benchmark the Hierarchical Temporal Defense (HTD) framework on the BrainChip Akida AKD1000 processor against a suite of adversarial temporal attacks. We demonstrate that unlike traditional deep learning defenses which often degrade efficiency significantly with increased robustness, the event-driven nature of the proposed architecture achieves a superior trade-off. The system reduces gradient-based adversarial success rates from 82.1% to 18.7% and temporal jitter success rates from 75.8% to 25.1%, while maintaining an energy consumption of approximately 45 microjoules per inference. We report a counter-intuitive reduction in dynamic power consumption in the fully defended configuration, attributed to volatility-gated plasticity mechanisms that induce higher network sparsity. These results provide empirical evidence that neuromorphic sparsity enables sustainable and high-assurance edge autonomy.

Sylvester Kaczmarek

Department of Computing, Imperial College London, London, United Kingdom

Adversarial RobustnessNeuromorphic ComputingEnergy-Efficient MLRoboticsAdversarial Attacks
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Mar 13, 2026

Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes

Federated learning on neuromorphic hardware remains unexplored because on-chip spike-timing-dependent plasticity (STDP) produces binary weight updates rather than the floating-point gradients assumed by standard algorithms. We build a two-node federated system with BrainChip Akida AKD1000 processors and run approximately 1,580 experimental trials across seven analysis phases. Of four weight-exchange strategies tested, neuron-level concatenation (FedUnion) consistently preserves accuracy while element-wise weight averaging (FedAvg) destroys it (p = 0.002). Domain-adaptive fine-tuning of the upstream feature extractor accounts for most of the accuracy gains, confirming feature quality as the dominant factor. Scaling feature dimensionality from 64 to 256 yields 77.0% best-strategy federated accuracy (n=30, p < 0.001). Two independent asymmetries (wider features help federation more than individual learning, while binarization hurts federation more) point to a shared prototype complementarity mechanism: cross-node transfer scales with the distinctiveness of neuron prototypes.

Steven Motta, Gioele Nanni

Federated LearningFederated Learning AggregationNeuromorphic ComputingFederated Fine-Tuning
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Mar 13, 2026

SRAM-Based Compute-in-Memory Accelerator for Linear-decay Spiking Neural Networks

Spiking Neural Networks (SNNs) have emerged as a biologically inspired alternative to conventional deep networks, offering event-driven and energy-efficient computation. However, their throughput remains constrained by the serial update of neuron membrane states. While many hardware accelerators and Compute-in-Memory (CIM) architectures efficiently parallelize the synaptic operation (W x I) achieving O(1) complexity for matrix-vector multiplication, the subsequent state update step still requires O(N) time to refresh all neuron membrane potentials. This mismatch makes state update the dominant latency and energy bottleneck in SNN inference. To address this challenge, we propose an SRAM-based CIM for SNN with Linear Decay Leaky Integrate-and-Fire (LD-LIF) Neuron that co-optimizes algorithm and hardware. At the algorithmic level, we replace the conventional exponential membrane decay with a linear decay approximation, converting costly multiplications into simple additions while accuracy drops only around 1%. At the architectural level, we introduce an in-memory parallel update scheme that performs in-place decay directly within the SRAM array, eliminating the need for global sequential updates. Evaluated on benchmark SNN workloads, the proposed method achieves a 1.1 x to 16.7 x reduction of SOP energy consumption, while providing 15.9 x to 69 x more energy efficiency, with negligible accuracy loss relative to original decay models. This work highlights that beyond accelerating the (W x I) computation, optimizing state-update dynamics within CIM architectures is essential for scalable, low-power, and real-time neuromorphic processing.

Hongyang Shang, Shuai Dong, Yahan Yang +3

Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China

LIF NeuronsSpiking Neural NetworksEfficient Neural Network InferenceNeuromorphic ComputingCompute-in-Memory
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Mar 12, 2026

SNAP-V: A RISC-V SoC with Configurable Neuromorphic Acceleration for Small-Scale Spiking Neural Networks

Spiking Neural Networks (SNNs) have gained significant attention in edge computing due to their low power consumption and computational efficiency. However, existing implementations either use conventional System on Chip (SoC) architectures that suffer from memory-processor bottlenecks, or large-scale neuromorphic hardware that is inefficient and wasteful for small-scale SNN applications. This work presents SNAP-V, a RISC-V-based neuromorphic SoC with two accelerator variants: Cerebra-S (bus-based) and Cerebra-H (Network-on-Chip (NoC)-based) which are optimized for small-scale SNN inference, integrating a RISC-V core for management tasks, with both accelerators featuring parallel processing nodes and distributed memory. Experimental results show close agreement between software and hardware inference, with an average accuracy deviation of 2.62% across multiple network configurations, and an average synaptic energy of 1.05 pJ per synaptic operation (SOP) in 45 nm CMOS technology. These results show that the proposed solution enables accurate, energy-efficient SNN inference suitable for real-time edge applications.

Kanishka Gunawardana, Sanka Peeris, Kavishka Rambukwella +4

Department of Computer Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka

Neuromorphic ComputingEfficient Neural Network InferenceSpiking Neural NetworksEdge Computing
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Mar 12, 2026

Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks

Although the temporal spike dynamics of spiking neural networks (SNNs) enable low-power temporal pattern capture capabilities, they also incur inherent inconsistencies that severely compromise representation. In this paper, we perform dual consistency optimization via Stable Spike to mitigate this problem, thereby improving the recognition performance of SNNs. With the hardware-friendly ``AND" bit operation, we efficiently decouple the stable spike skeleton from the multi-timestep spike maps, thereby capturing critical semantics while reducing inconsistencies from variable noise spikes. Enforcing the unstable spike maps to converge to the stable spike skeleton significantly improves the inherent consistency across timesteps. Furthermore, we inject amplitude-aware spike noise into the stable spike skeleton to diversify the representations while preserving consistent semantics. The SNN is encouraged to produce perturbation-consistent predictions, thereby contributing to generalization. Extensive experiments across multiple architectures and datasets validate the effectiveness and versatility of our method. In particular, our method significantly advances neuromorphic object recognition under ultra-low latency, improving accuracy by up to 8.33%. This will help unlock the full power consumption and speed potential of SNNs.

Yongqi Ding, Kunshan Yang, Linze Li +3

School of Information and Software Engineering University of Electronic Science and Technology of China

Spiking Neural NetworksNeuromorphic ComputingConsistency Regularization
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Mar 10, 2026

Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computing

The potential for neuromorphic computing to provide intrinsic fault tolerance has long been speculated, but the brain's robustness in neuromorphic applications has yet to be demonstrated. Here, we show that a previously described, natively spiking neuromorphic algorithm for solving partial differential equations is intrinsically tolerant to structural perturbations in the form of ablated neurons and dropped spikes. The tolerance band for these perturbations is large: we find that as many as 32 percent of the neurons and up to 90 percent of the spikes may be entirely dropped before a significant degradation in the accuracy results. Furthermore, this robustness is tunable through structural hyperparameters. This work demonstrates that the specific brain-like inspiration behind the algorithm contributes to a significant degree of robustness expected from brain-like neuromorphic algorithms.

Bradley H. Theilman, James B. Aimone

Neural Exploration & Research Laboratory, Sandia National Laboratories, Albuquerque, NM

Neuromorphic ComputingNeural Network RobustnessNeural PDE SolversSpiking Neural Networks
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Mar 10, 2026

DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data

Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by computing event-based. However, they have trouble decoding temporal information with high accuracy. Thus, they commonly resort to recurrence or delays to enhance their temporal computing ability which, however, bring downsides in terms of hardware-efficiency. In the brain, dendrites are computational powerhouses that just recently started to be acknowledged in such machine learning systems. In this work, we focus on a sequence detection mechanism present in branches of dendrites and translate it into a novel type of neural network by introducing a dendrocentric neural network, DendroNN. DendroNNs identify unique incoming spike sequences as spatiotemporal features. This work further introduces a rewiring phase to train the non-differentiable spike sequences without the use of gradients. During the rewiring, the network memorizes frequently occurring sequences and additionally discards those that do not contribute any discriminative information. The networks display competitive accuracies across various event-based time series datasets. We also propose an asynchronous digital hardware architecture using a time-wheel mechanism that builds on the event-driven design of DendroNNs, eliminating per-step global updates typical of delay- or recurrence-based models. By leveraging a DendroNN's dynamic and static sparsity along with intrinsic quantization, it achieves up to 4x higher efficiency than state-of-the-art neuromorphic hardware at comparable accuracy on the same audio classification task, demonstrating its suitability for spatiotemporal event-based computing. This work offers a novel approach to low-power spatiotemporal processing on event-driven hardware.

Jann Krausse, Zhe Su, Kyrus Mama +4

Infineon Technologies, Dresden, Germany · Karlsruhe Institute of Technology, Karlsruhe, Germany · Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland +2

Efficient Neural Network InferenceSpiking Neural NetworksNeuromorphic Computing
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Feb 27, 2026

Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks

Continuous-time, event-native spiking neural networks (SNNs) operate strictly on spike events, treating spike timing and ordering as the representation rather than an artifact of time discretization. This viewpoint aligns with biological computation and with the native resolution of event sensors and neuromorphic processors, while enabling compute and memory that scale with the number of events. However, two challenges hinder practical, end-to-end trainable event-based SNN systems: 1) exact charge--fire--reset dynamics impose inherently sequential processing of input spikes, and 2) precise spike times must be solved without time bins. We address both. First, we use parallel associative scans to consume multiple input spikes at once, yielding up to 44x speedups over sequential simulation while retaining exact hard-reset dynamics. Second, we implement differentiable spike time solvers that compute spike times to machine-precision without discrete-time approximations or restrictive analytic assumptions. We demonstrate the viability of training SNNs using our solutions on four event-based datasets on GPUs.

Todd Morrill, Christian Pehle, Anthony Zador

Department of Computer Science, Columbia University, New York, NY, USA · Department of Neuroscience, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

Spiking Neural NetworksNeuromorphic Computing
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Feb 26, 2026

Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision

Limitations on the availability of Dynamic Vision Sensors (DVS) present a fundamental challenge to researchers of neuromorphic computer vision applications. In response, datasets have been created by the research community, but often contain a limited number of samples or scenarios. To address the lack of a comprehensive simulator of neuromorphic vision datasets, we introduce the Anomalous Neuromorphic Tool for Shapes (ANTShapes), a novel dataset simulation framework. Built in the Unity engine, ANTShapes simulates abstract, configurable 3D scenes populated by objects displaying randomly-generated behaviours describing attributes such as motion and rotation. The sampling of object behaviours, and the labelling of anomalously-acting objects, is a statistical process following central limit theorem principles. Datasets containing an arbitrary number of samples can be created and exported from ANTShapes, along with accompanying label and frame data, through the adjustment of a limited number of parameters within the software. ANTShapes addresses the limitations of data availability to researchers of event-based computer vision by allowing for the simulation of bespoke datasets to suit purposes including object recognition and localisation alongside anomaly detection.

Mike Middleton, Teymoor Ali, Hakan Kayan +6

Bio-Inspired Systems and Technologies Lab, University of York, York, UK · Faculty of Natural Sciences, University of Stirling, Stirling, UK · School of Computer Science and Informatics, Cardiff University, Cardiff, UK +2

Synthetic Anomaly GenerationEvent-Based VisionNeuromorphic ComputingAnomaly Detection
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Feb 25, 2026

Orthogonal Weight Modification Enhances Learning Scalability and Convergence Efficiency without Gradient Backpropagation

Recognizing the substantial computational cost of backpropagation (BP), non-BP methods have emerged as attractive alternatives for efficient learning on emerging neuromorphic systems. However, existing non-BP approaches still face critical challenges in efficiency and scalability. Inspired by neural representations and dynamic mechanisms in the brain, we propose a perturbation-based approach called LOw-rank Cluster Orthogonal (LOCO) weight modification. We find that low-rank is an inherent property of perturbation-based algorithms. Under this condition, the orthogonality constraint limits the variance of the node perturbation (NP) gradient estimates and enhances the convergence efficiency. Through extensive evaluations on multiple datasets, LOCO demonstrates the capability to locally train the deepest spiking neural networks to date (more than 10 layers), while exhibiting strong continual learning ability, improved convergence efficiency, and better task performance compared to other brain-inspired non-BP algorithms. Notably, LOCO requires only O(1) parallel time complexity for weight updates, which is significantly lower than that of BP methods. This offers a promising direction for achieving high-performance, real-time, and lifelong learning on neuromorphic systems.

Guoqing Ma, Shan Yu

Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Future Technology, University of Chinese Academy of Sciences · Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences

Neuromorphic ComputingSpiking Neural Networks
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Feb 24, 2026

ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding

Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision boundaries and, consequently, vulnerability to outliers, adversarial perturbations, and out-of-distribution inputs. To address these limitations, we introduce ConformalHDC, a unified framework that combines the statistical guarantees of conformal prediction with the computational efficiency of HDC. For this framework, we propose two complementary variations. First, the set-valued formulation provides finite-sample, distribution-free coverage guarantees. Using carefully designed conformity scores, it forms enclosed decision boundaries that improve robustness to non-conforming inputs. Second, the point-valued formulation leverages the same conformity scores to produce a single prediction when desired, potentially improving accuracy over traditional HDC by accounting for class interactions. We demonstrate the broad applicability of the proposed framework through evaluations on multiple real-world datasets. In particular, we apply our method to the challenging problem of decoding non-spatial stimulus information from the spiking activity of hippocampal neurons recorded as subjects performed a sequence memory task. Our results show that ConformalHDC not only accurately decodes the stimulus information represented in the neural activity data, but also provides rigorous uncertainty estimates and correctly abstains when presented with data from other behavioral states. Overall, these capabilities position the framework as a reliable, uncertainty-aware foundation for neuromorphic computing.

Ziyi Liang, Hamed Poursiami, Zhishun Yang +5

Department of Statistics, UC Irvine, Irvine, CA, USA · Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA · Center for the Neurobiology of Learning and Memory, UC Irvine, Irvine, CA, USA +2

Hyperdimensional ComputingNeuromorphic ComputingConformal PredictionNeural DecodingOOD Detection
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Feb 24, 2026

Robust Spiking Neural Networks Against Adversarial Attacks

Spiking Neural Networks (SNNs) represent a promising paradigm for energy-efficient neuromorphic computing due to their bio-plausible and spike-driven characteristics. However, the robustness of SNNs in complex adversarial environments remains significantly constrained. In this study, we theoretically demonstrate that those threshold-neighboring spiking neurons are the key factors limiting the robustness of directly trained SNNs. We find that these neurons set the upper limits for the maximum potential strength of adversarial attacks and are prone to state-flipping under minor disturbances. To address this challenge, we propose a Threshold Guarding Optimization (TGO) method, which comprises two key aspects. First, we incorporate additional constraints into the loss function to move neurons' membrane potentials away from their thresholds. It increases SNNs' gradient sparsity, thereby reducing the theoretical upper bound of adversarial attacks. Second, we introduce noisy spiking neurons to transition the neuronal firing mechanism from deterministic to probabilistic, decreasing their state-flipping probability due to minor disturbances. Extensive experiments conducted in standard adversarial scenarios prove that our method significantly enhances the robustness of directly trained SNNs. These findings pave the way for advancing more reliable and secure neuromorphic computing in real-world applications.

Shuai Wang, Malu Zhang, Yulin Jiang +7

University of Electronic Science and Technology of China · Shenzhen Loop Area Institute · Northumbria University +1

Adversarial RobustnessSpiking Neural NetworksNeuromorphic Computing
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Feb 21, 2026

Memory-Augmented Spiking Networks: Synergistic Integration of Complementary Mechanisms for Neuromorphic Vision

Spiking Neural Networks (SNNs) provide biological plausibility and energy efficiency, yet systematic investigations of memory augmentation strategies remain limited. We conduct a five-model ablation study integrating Leaky Integrate-and-Fire neurons, Supervised Contrastive Learning (SCL), Hopfield networks, and Hierarchical Gated Recurrent Networks (HGRN) on the N-MNIST dataset. Baseline SNNs exhibit organized neuronal groupings, or structured assemblies, characterized by a silhouette score of 0.687±0.0120.687 \pm 0.012. Individual augmentations introduce trade-offs: SCL improves accuracy by 0.28%0.28\% but reduces clustering (silhouette score 0.637±0.0150.637 \pm 0.015), while HGRN yields consistent gains in both accuracy (+1.01%+1.01\%) and computational efficiency (170.6×170.6\times). Full integration achieves a balanced improvement across metrics, reaching a silhouette score of 0.715±0.0080.715 \pm 0.008, classification accuracy of 97.49±0.10%97.49 \pm 0.10\%, energy consumption of 1.85±0.06 μJ1.85 \pm 0.06\,μ\mathrm{J}, and sparsity of 97.0%97.0\%. These results indicate that optimal performance emerges from architectural balance rather than isolated optimization, establishing design principles for memory-augmented neuromorphic systems.

Effiong Blessing, Chiung-Yi Tseng, Isaac Nkrumah +1

Department Of Computer Science, Saint Louis University · Luxmuse AI · Saint Louis University +1

Spiking Neural NetworksNeuromorphic ComputingImage Classification
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Feb 20, 2026

Flexi-NeurA: A Flexible Neuromorphic Accelerator with Adaptive Bit-Precision Exploration for Edge Devices

Neuromorphic accelerators promise unparalleled energy efficiency and computational density for spiking neural networks, especially in wearable biomedical devices and neural prosthetics where power constraints are stringent. However, most existing platforms exhibit rigid architectures with limited configurability, restricting their adaptability to heterogeneous biological signals and diverse design objectives. To address these limitations, we present Flexi-NeurA--a flexible neuromorphic accelerator that unifies configurability and efficiency. Flexi-NeurA allows users to customize neuron models, network structures, and precision settings at design time. By pairing these design-time configurability features with a time-multiplexed and event-driven processing approach, Flexi-NeurA reduces the required hardware resources and total power while preserving high efficiency and low inference latency. Complementing this, we introduce Flex-plorer, a design-space exploration tool that determines cost-effective fixed-point precisions for critical parameters--such as decay factors, synaptic weights, and membrane potentials--based on user-defined trade-offs between accuracy and resource usage. Based on the configuration selected through the Flex-plorer process, RTL code is configured to match the specified design. Comprehensive evaluations across distinct domains--biomedical auditory processing, dynamic vision sensor gesture recognition, and standard vision classification--demonstrate that the hardware/software co-framework successfully balances accuracy and power budgets for diverse applications. A 3-layer 256-128-10 fully connected network with LIF neurons mapped onto two processing cores achieves 96.23% accuracy on MNIST with 1.1 ms inference latency, utilizing only 1,623 logic cells, 7 BRAMs, and 111 mW of total power--demonstrating superior resource efficiency compared to SoTA hardware baselines.

Mohammad Farahani, Mohammad Rasoul Roshanshah, Saeed Safari

High-Performance Embedded Architecture Laboratory (HiPEAL), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

Neuromorphic ComputingFPGA AccelerationSpiking Neural NetworksNeural Network Quantization
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Feb 20, 2026

HiAER-Spike Software-Hardware Reconfigurable Platform for Event-Driven Neuromorphic Computing at Scale

In this work, we present HiAER-Spike, a modular, reconfigurable, event-driven neuromorphic computing platform designed to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster than real time. This system, assembled at the UC San Diego Supercomputer Center, comprises a co-designed hard- and software stack that is optimized for run-time massively parallel processing and hierarchical address-event routing (HiAER) of spikes while promoting memory-efficient network storage and execution. The architecture efficiently handles both sparse connectivity and sparse activity for robust and low-latency event-driven inference for both edge and cloud computing. A Python programming interface to HiAER-Spike, agnostic to hardware-level detail, shields the user from complexity in the configuration and execution of general spiking neural networks with minimal constraints in topology. The system is made easily available over a web portal for use by the wider community. In the following, we provide an overview of the hard- and software stack, explain the underlying design principles, demonstrate some of the system's capabilities and solicit feedback from the broader neuromorphic community. Examples are shown demonstrating HiAER-Spike's capabilities for event-driven vision on benchmark CIFAR-10, DVS event-based gesture, MNIST, and Pong tasks.

Gwenevere Frank, Gopabandhu Hota, Keli Wang +12

Institute for Neural Computation, UC San Diego, 9500 Gilman Dr, La Jolla, 92093, CA, USA. · Qernel AI, 460 California Ave ste 204, Palo Alto, 94306, CA, USA. · Fujitsu, 4-1-1 Kamikodanaka, Nakahara-ku Kawasaki-shi, 211-8588, Kanagawa, Japan. +1

Neuromorphic ComputingEfficient Neural Network InferenceSpiking Neural Networks
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