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

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

Apr 30, 2026

NeuroRing: Scaling Spiking Neural Networks via Multi-FPGA Bidirectional Ring Topologies and Stream-Dataflow Architectures

Spiking neural networks (SNNs) are a promising paradigm for energy-efficient event-driven computation, but large-scale SNN execution remains challenging because sparse spike communication and synchronization can dominate runtime. Existing solutions across CPU, GPU, ASIC, and FPGA platforms offer different trade-offs between programmability, efficiency, and scalability. To address this gap, we present NeuroRing, a modular and scalable SNN accelerator based on a stream-dataflow architecture and a bidirectional ring topology, implemented in High-Level Synthesis (HLS) on FPGAs. NeuroRing supports modular single- and multi-FPGA deployment and is compatible with existing SNN workflows through integration with the NEST simulator. We evaluate NeuroRing on the cortical microcircuit benchmark and a Sudoku constraint-satisfaction workload. Results show that NeuroRing preserves the key activity statistics of the NEST reference model, achieves faster-than-real-time execution of the full-scale cortical microcircuit with a real-time factor (RTF) of 0.83, exhibits meaningful strong and weak scaling, and provides competitive energy efficiency on two programmable FPGAs. These results position NeuroRing as a flexible and scalable platform for both neuroscience simulation and broader event-driven applications.

Muhammad Ihsan Al Hafiz, Artur Podobas

KTH Royal Institute of Technology, Stockholm, Sweden

Spiking Neural Networks Neuromorphic Computing FPGA-Based Neural Network Acceleration

Apr 29, 2026

EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures

We propose EdgeSpike, a co-designed spiking neural network (SNN) framework for autonomous low-power sensing in edge Internet of Things (IoT) architectures. EdgeSpike unifies (i) a hybrid surrogate-gradient and direct-encoding training pipeline, (ii) a hardware-aware neural architecture search (NAS) bounded by per-inference energy and memory budgets, (iii) an event-driven runtime targeting Intel Loihi 2, SpiNNaker 2, and commodity ARM Cortex-M microcontrollers with custom spike-sparse SIMD kernels, and (iv) a lightweight local plasticity rule enabling continual on-device adaptation without backpropagation. The framework is evaluated across five sensing tasks (keyword spotting, vibration-based machine fault detection, surface electromyography gesture recognition, 77 GHz radar human-activity classification, and structural-health acoustic-emission monitoring) on three hardware targets. EdgeSpike achieves a mean classification accuracy of 91.4%, within 1.2 percentage points (pp) of strong INT8 convolutional neural network (CNN) baselines (mean 92.6%), while reducing energy per inference by 18x to 47x on neuromorphic hardware (mean 31x) and by 4.6x to 7.9x on Cortex-M (mean 6.1x). End-to-end latency remains at or below 9.4 ms across all 15 task-hardware configurations. A seven-month, 64-node wireless field deployment confirms a 6.3x extension in projected battery lifetime (from 312 to 1978 days at 2 Wh per node) and bounded accuracy degradation under seasonal drift (0.7 pp with on-device adaptation versus 2.1 pp without). Hardware-aware NAS evaluates 8400 candidates and yields a 12-point Pareto front. EdgeSpike will be released as open source with reproducible training pipelines, hardware-portable runtimes, and benchmark suites.

Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov, Taner Yilmaz

Department of Economics, Stockholm University, Universitetsvägen 10 A, SE-106 91 Stockholm, Sweden · Department of Computer Engineering, Afyon Kocatepe University, 03200 Afyonkarahisar, Türkiye

Energy-Efficient ML Edge Inference Internet of Things Spiking Neural Networks Neuromorphic Computing

Apr 29, 2026

Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks

Anomaly detection in dynamic networks is critical for applications from cybersecurity to industrial monitoring, yet existing methods face challenges in energy efficiency, temporal precision, and adaptability. This paper introduces ASTDP-GAD, a novel Adaptive Spiking Temporal Dynamics Plasticity framework for Graph Anomaly Detection that integrates spiking graph neural networks with STDP learning for energy-efficient neuromorphic detection in dynamic networks. Our framework unifies spiking neural computation, STDP learning, and graph-based anomaly detection through the following key innovations: temporal spike graph encoding with adaptive Leaky Integrate-and-Fire (LIF) dynamics; LIF-based graph attention with lateral inhibition; event-driven hypergraph memory with STDP-inspired prototype updates; spike rate contrast pooling based on spiking irregularity; adaptive STDP layers capturing causal temporal relationships; and multi-scale temporal convolution with multi-factor anomaly fusion. Theoretical analysis provides rigorous guarantees: spike encoding preserves input information with resolution scaling linearly in simulation steps and hidden dimension; LIFGAT approximates any continuous attention function; hypergraph memory converges to optimal prototypes; contrast pooling achieves provable anomaly selection bounds; STDP learning converges stably; and multi-factor fusion produces calibrated scores with up to

5\times

variance reduction. Extensive experiments on nine datasets on both dynamic and static graphs demonstrate superior anomaly detection accuracy while maintaining biological plausibility and energy efficiency for neuromorphic deployment.

Abdul Joseph Fofanah, Lian Wen, David Chen +2

School of ICT, Griffith University, Australia

Graph Neural Networks Spiking Neural Networks Spike-Timing-Dependent Plasticity Neuromorphic Computing Anomaly Detection

Apr 28, 2026

Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis

This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from existing approaches that use frame-based cameras with fixed exposure times, which trade off signal-to-noise ratio for temporal resolution, we introduce computational neuromorphic tracking (CNT), a physics-informed framework that combines asynchronous event sensing with task-driven speckle analysis for robust motion estimation. We formulate the neuromorphic speckle aggregation as a spatiotemporal speckle representation, jointly optimizing the temporal and spatial parameters to maximize tracking stability under extreme conditions. Extensive experiments demonstrate that our method enables robust motion tracking of 10x faster motion and under 10x dimmer illumination compared to conventional systems. These improvements significantly broaden the operational regime for tracking through scattering media, providing an efficient and scalable solution for demanding scenarios involving rapid motion and low-light conditions.

Yuqing Cao, Shuo Zhu, Rongzhou Chen +3

Department of Electrical and Computer Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China.

Event-Based Vision Neuromorphic Computing

Apr 24, 2026

SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference

Scaling context length is reshaping large-model development, yet full-attention Transformers suffer from prohibitive computation and inference bottlenecks at long sequences. A key challenge is to design foundation models that maintain performance and long-context efficiency with minimal training overhead. We introduce SpikingBrain2.0 (SpB2.0), a 5B model that advances both architecture and training efficiency of its predecessor. Our contributions are two-fold. (1) Architectural Innovation: We propose Dual-Space Sparse Attention (DSSA), an inter-layer hybrid of Sparse Softmax Attention (MoBA) and Sparse Linear Attention (SSE), achieving an improved performance-efficiency trade-off for long-context modeling. SpB2.0 further supports dual quantization paths: INT8-Spiking coding enables sparse event-driven computation, while FP8 coding accelerates inference on modern GPUs. (2) Enhanced Training Strategy: We develop an optimized Transformer-to-Hybrid (T2H) pipeline with dual conversion paths for LLMs and VLMs using curated open-source data. Empirically, SpB2.0-5B and SpB2.0-VL-5B recover most of the base Transformer (Qwen3-4B) capability with under 7k A100 GPU hours. SpB2.0 achieves a 10.13x TTFT speedup at 4M context and supports over 10M tokens on 8 A100 GPUs under vLLM, where full-attention models exceed memory limits. It also demonstrates strong cross-platform compatibility, enabling FP8 GPU inference (2.52x speedup at 250k) and efficient neuromorphic execution (64.31% sparsity, with 70.6% and 46.5% area and power reduction at 500MHz). Overall, SpikingBrain2.0 provides a practical pathway for lightweight, multimodal, spiking foundation models, highlighting the potential of combining brain-inspired mechanisms with efficient architectures for resource-constrained and edge scenarios.

Yuqi Pan, Jinghao Zhuang, Yupeng Feng +16

Institute of Automation, Chinese Academy of Sciences · The Hong Kong Polytechnic University · Beijing Key Laboratory of Brain-Inspired General Intelligence Large Model +1

Long-Context Language Model Inference Spiking Neural Networks Sparse Attention Neuromorphic Computing

Apr 24, 2026

ROSA: Robust and Energy-Efficient Microring-Based Optical Neural Networks via Optical Shift-and-Add and Layer-Wise Hybrid Mapping

This work presents ROSA, a microring-based optical neural network architecture that improves robustness and energy efficiency using an optical shift-and-add (OSA) module and a layer-wise hybrid mapping strategy. It introduces a noise-aware voltage-to-weight model considering DAC and thermal variations, and a workload-aware framework to co-optimize MRR array size and layer-wise dataflow. Optimized arrays reduce the aggregated relative energy-delay product (EDP) by 64% and 26% compared with DEAP-CNNs and a general compact array, respectively. OSA further contributes 29% EDP reduction. The proposed hybrid mapping strategy improves CIFAR-10 accuracy by 8.3% over weight-stationary mapping while achieving an average 54.7% lower EDP than DEAP-CNNs.

Huifan Zhang, Yun Hu, Caizhi Sheng +2

ShanghaiTech University, Shanghai, China

Energy-Efficient ML Neural Network Robustness Neuromorphic Computing

Apr 23, 2026

Neuromorphic Computing Based on Parametrically-Driven Oscillators and Frequency Combs

Parametrically driven oscillators exhibit distinct dynamical regimes that strongly influence their computational capability as physical reservoirs. Here, we demonstrate reservoir computing in a two-mode system exhibiting 2:1 parametric resonance using Mackey--Glass, Rössler, and Lorenz chaotic time-series prediction tasks. Mapping the normalized mean squared error over parameter space reveals that the lowest prediction errors consistently occur within the parametric resonance regime, where nonlinear interactions are activated while temporal coherence is preserved. In contrast, although frequency-comb states increase spectral dimensionality, prediction performance degrades in the chaotic comb regime due to loss of phase coherence. We further show that the modulation depth

Δ\text{F}

, detuning

κ

, damping ratio

γ_{21}

, and input data rate systematically reshape the accessible dynamical regimes and thereby control the reservoir performance. These results identify parametric resonance as a robust operating regime for oscillator-based neuromorphic computing and establish dynamical regime engineering as a route toward physical reservoir optimization.

Mahadev Sunil Kumar, Adarsh Ganesan

Amrita School of Computing, Amrita Vishwa Vidyapeetham, Kollam, India 690525 · Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani – Dubai Campus, Dubai, United Arab Emirates 345055

Dynamical Systems Reservoir Computing Neuromorphic Computing Chaotic Time-Series Prediction

Apr 23, 2026

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor-based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time-series prediction and image recognition. Although RC has been demonstrated with several memristive devices, a comprehensive evaluation of device-level requirements remains limited. In this paper, we analyze and explain the operation of a parallel delayed feedback network (PDFN) RC architecture with volatile memristors, focusing on how device characteristics -- such as decay rate, quantization, and variability -- affect reservoir performance. We further discuss strategies to improve data representation in the reservoir using preprocessing methods and suggest potential improvements. The proposed approach achieves 95.89% classification accuracy on MNIST, comparable with the best reported memristor-based RC implementations. Furthermore, the method maintains high robustness under 20% device variability, achieving an accuracy of up to 94.2%. These results demonstrate that volatile memristors can support reliable spatio-temporal information processing and reinforce their potential as key building blocks for compact, high-speed, and energy-efficient neuromorphic computing systems.

Rishona Daniels, Duna Wattad, Ronny Ronen +2

Viterbi Faculty of Electrical and Computing Engineering, Technion - Israel Institute of Technology, Haifa, Israel · College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, United Kingdom

Reservoir Computing Neuromorphic Computing Image Classification

Apr 21, 2026

Scalable Memristive-Friendly Reservoir Computing for Time Series Classification

Memristive devices present a promising foundation for next-generation information processing by combining memory and computation within a single physical substrate. This unique characteristic enables efficient, fast, and adaptive computing, particularly well suited for deep learning applications. Among recent developments, the memristive-friendly echo state network (MF-ESN) has emerged as a promising approach that combines memristive-inspired dynamics with the training simplicity of reservoir computing, where only the readout layer is learned. Building on this framework, we propose memristive-friendly parallelized reservoirs (MARS), a simplified yet more effective architecture that enables efficient scalable parallel computation and deeper model composition through novel subtractive skip connections. This design yields two key advantages: substantial training speedups of up to 21x over the inherently lightweight echo state network baseline and significantly improved predictive performance. Moreover, MARS demonstrates what is possible with parallel memristive-friendly reservoir computing: on several long sequence benchmarks our compact gradient-free models substantially outperform strong gradient-based sequence models such as LRU, S5, and Mamba, while reducing full training time from minutes or hours down seconds or even only a few hundred milliseconds. Our work positions parallel memristive-friendly computing as a promising route towards scalable neuromorphic learning systems that combine high predictive capability with radically improved computational efficiency, while providing a clear pathway to energy-efficient, low-latency implementations on emerging memristive and in-memory hardware.

Coşku Can Horuz, Andrea Ceni, Claudio Gallicchio +1

Adaptive AI Research Group University of Lübeck, Germany · Department of Computer Science University of Pisa, Italy

Dynamic Neural Networks Energy-Efficient ML Time Series Classification Reservoir Computing Neuromorphic Computing

Apr 20, 2026

Ternary Memristive Logic: Hardware for Reasoning Realized via Domain Algebra

Memristive crossbars store numerical weights needing aggregation and decoding; a single junction means nothing alone. This paper presents a fundamentally different use: each junction stores a complete, domain-scoped logical assertion (holds/negated/undefined). Ternary resistance states encode these values directly. We establish a structure-preserving mapping from a domain algebra to crossbar topology: domains become isolated arrays, specialization becomes directed wiring, relation typing controls inheritance gates, and cross-domain links become explicit registers. The physical layout thus embodies the algebra; changing wiring changes reasoning semantics. We detail an ICD-11 respiratory disease classification chip (1,247 entities, ~136k 1T1R junctions) enabling domain scoping, three-valued logic, transitive cascade, typed inheritance, and cross-axis queries. Behavioral simulation (sigma_log=0.15, SNR=20dB) shows error-free operation across 100,000 trials per task with wide tolerance margins. Where prior work unified representation and computation in software, this work unifies them in hardware: reading one junction answers one question, without symbolic interpretation.

Chao Li

Logical Reasoning Knowledge Representation Neuromorphic Computing Knowledge Graphs

Apr 19, 2026

Fully Analog Resonant Recurrent Neural Network via Metacircuit

Physical neural networks offer a transformative route to edge intelligence, providing superior inference speed and energy efficiency compared to conventional digital architectures. However, realizing scalable, end-to-end, fully analog recurrent neural networks for temporal information processing remains challenging due to the difficulty of faithfully mapping trained network models onto physical hardware. Here we present a fully analog resonant recurrent neural network (R

^2

NN) implemented via a metacircuit architecture composed of coupled electrical local resonators. A reformulated mechanical-electrical analogy establishes a direct mapping between the R

^2

NN model and metacircuit elements, enabling accurate physical implementation of trained neural network parameters. By integrating jointly trainable global resistive coupling and local resonances, which generate effective frequency-dependent negative resistances, the architecture shapes an impedance landscape that steers currents along frequency-selective pathways. This mechanism enables direct extraction of discriminative spectral features, facilitating real-time temporal classification of raw analog inputs while bypassing analog-to-digital conversion. We demonstrate the cross-domain versatility of this framework using integrated hardware for tactile perception, speech recognition, and condition monitoring. This work establishes a scalable, fully analog paradigm for intelligent temporal processing and paves the way for low-latency, resource-efficient physical neural hardware for edge intelligence.

Zixin Zhou, Tianxi Jiang, Menglong Yang +3

Institute of Humanoid Robots, CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, People’s Republic of China · State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China

Efficient Neural Network Inference Time Series Classification Recurrent Neural Networks Neuromorphic Computing Edge ML Physical Neural Networks

Apr 17, 2026

Closing the Theory-Practice Gap in Spiking Transformers via Effective Dimension

Spiking transformers achieve competitive accuracy with conventional transformers while offering

38

-

57\times

energy efficiency on neuromorphic hardware, yet no theoretical framework guides their design. This paper establishes the first comprehensive expressivity theory for spiking self-attention. We prove that spiking attention with Leaky Integrate-and-Fire neurons is a universal approximator of continuous permutation-equivariant functions, providing explicit spike circuit constructions including a novel lateral inhibition network for softmax normalization with proven

O(1/\sqrt{T})

convergence. We derive tight spike-count lower bounds via rate-distortion theory:

\varepsilon

-approximation requires

Ω(L_f^2 nd/\varepsilon^2)

spikes, with rigorous information-theoretic derivation. Our key insight is input-dependent bounds using measured effective dimensions (

d_{\text{eff}}=47

--

89

for CIFAR/ImageNet), explaining why

T=4

timesteps suffice despite worst-case

T \geq 10{,}000

predictions. We provide concrete design rules with calibrated constants (

C=2.3

, 95% CI:

[1.9, 2.7]

). Experiments on Spikformer, QKFormer, and SpikingResformer across vision and language benchmarks validate predictions with

R^2=0.97

(

p<0.001

). Our framework provides the first principled foundation for neuromorphic transformer design.

Dongxin Guo, Jikun Wu, Siu Ming Yiu

*The University of Hong Kong, Hong Kong, China · †Brain Investing Limited, Hong Kong, China

Spiking Transformers Transformer Attention Spiking Neural Networks Neuromorphic Computing Neural Network Approximation Theory

Apr 17, 2026

Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks

Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal processing from physics enforcement: a three-layer leaky integrate-and-fire SNN estimates passive component parameters while a differentiable ODE solver provides physics-consistent training by decoupling the ODE physics loss from the unrolled spiking loop. On an EMI-corrupted synchronous buck converter benchmark, the SNN reduces lumped resistance error from

25.8\%

to

10.2\%

versus a feedforward baseline, within the

\pm 10\%

manufacturing tolerance of passive components, at a projected

{\sim}270\times

energy reduction on neuromorphic hardware. Persistent membrane states further enable degradation tracking and event-driven fault detection via a

+5.5

percentage-point spike-rate jump at abrupt faults. With

93\%

spike sparsity, the architecture is suited for always-on deployment on Intel Loihi 2 or BrainChip Akida.

Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa +1

University of Wisconsin–Madison Madison, Wisconsin, USA · George Mason University Fairfax, Virginia, USA

Parameter Estimation Predictive Maintenance Fault Detection Spiking Neural Networks Neuromorphic Computing Physics-Informed ML

Apr 15, 2026

Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation

Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide a biologically inspired mechanism for encoding such variables, yet neuromorphic realizations have rarely addressed proprioceptive estimation under resource constraints. This work introduces a spiking ring-attractor network representing a robot joint angle through self-sustaining population activity. Local excitation and broad inhibition support a stable activity bump, while velocity-modulated asymmetries drive its translation and boundary conditions confine motion within mechanical limits. The network reproduces smooth trajectory tracking and remains stable near joint limits, showing reduced drift and improved accuracy compared to unbounded models. Such compact hardware-compatible implementation preserves multi-second stability demonstrating a near-linear relationship between bump velocity and synaptic modulation.

Federica Ferrari, Flavia Davidhi, Bernard Maacaron +6

Istituto Italiano di Tecnologia, Genoa, Italy · Department of Neurosurgery, University Hospital Zürich, Switzerland · Institute of Neuroinformatics, Univ. of Zurich and ETH Zurich, Switzerland +3

Continuous Attractor Neural Networks Robot State Estimation Spiking Neural Networks Neuromorphic Computing Recurrent Neural Networks

Apr 14, 2026

Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization

Continual learning, the ability to acquire new tasks sequentially without forgetting prior knowledge, is essential for deploying neural networks in dynamic real-world environments, from nuclear digital twin monitoring to grid-edge fault detection. Existing synaptic importance methods, such as Elastic Weight Consolidation (EWC) and Synaptic Intelligence (SI), rely on gradient computation, making them incompatible with neuromorphic hardware that lacks backpropagation support. We propose ISI-CV, the first gradient-free synaptic importance metric for SNN continual learning, derived from the Coefficient of Variation (CV) of Inter-Spike Intervals (ISIs). Neurons that fire regularly (low CV) encode stable, task-relevant features and are protected from overwriting; neurons with irregular firing are permitted to adapt freely. ISI-CV requires only spike time counters and integer arithmetic, all of which are native to every neuromorphic chip. We evaluate on four benchmarks of increasing difficulty: Split-MNIST, Permuted-MNIST, Split-FashionMNIST, and Split-N-MNIST using real Dynamic Vision Sensor (DVS) event data. Across three seeds, ISI-CV achieves zero forgetting (AF = 0.000 +/- 0.000) on Split-MNIST and Split-FashionMNIST, near-zero forgetting on Permuted-MNIST (AF = 0.001 +/- 0.000), and the highest accuracy with the lowest forgetting on real neuromorphic DVS data (AA = 0.820 +/- 0.012, AF = 0.221 +/- 0.014). On N-MNIST, gradient-based methods produce unreliable importance estimates and perform worse than no regularization; ISI-CV avoids this failure by design.

Samrendra Roy, Kazuma Kobayashi, Souvik Chakraborty +2

University of Illinois Urbana-Champaign, Department of Nuclear, Plasma & Radiological Engineering, Urbana, IL, USA · Indian Institute of Technology Delhi, Department of Applied Mechanics, New Delhi, India · University of Texas at El Paso, Department of Computer Science, El Paso, TX, USA +1

Spiking Neural Networks Catastrophic Forgetting Importance Weighting Neuromorphic Computing

Apr 13, 2026

Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems

Anomaly detection in nuclear industrial control systems (ICS) requires continuous, energy-efficient monitoring across multiple subsystems that are often deployed at different stages of plant commissioning. When a conventional neural network is sequentially trained to monitor new subsystems, it catastrophically forgets previously learned anomaly patterns, a safety-critical failure mode. We present the first spiking neural network (SNN)-based anomaly detection system with continual learning for nuclear ICS, addressing both challenges simultaneously. Our approach introduces spike-encoded asynchronous sensor fusion, a delta-based encoding that converts heterogeneous sensor streams into sparse spike trains at rates dictated by each sensor's natural dynamics, achieving 92.7% input sparsity. We evaluate five continual learning strategies, including sequential fine-tuning, Elastic Weight Consolidation (EWC), Synaptic Intelligence (SI), experience replay, and a hybrid EWC+Replay approach, on the HAI 21.03 nuclear ICS security dataset across three sequentially deployed subsystems (boiler, turbine, water treatment). The hybrid EWC+Replay method achieves an average F1 score of 0.979 with near-zero average forgetting (AF = 0.000 single seed; 0.035 +/- 0.039 across three seeds), while requiring 12.6x fewer operations (an estimated 2.5x in energy based on published hardware specifications) than an equivalent artificial neural network. The system detects all tested attacks with a mean latency of 0.6 seconds. These results demonstrate that neuromorphic computing offers a viable path toward always-on, energy-efficient, and adaptable safety monitoring for next-generation nuclear facilities.

Samrendra Roy, Sajedul Talukder, Syed Bahauddin Alam

University of Illinois Urbana-Champaign Department of Nuclear, Plasma & Radiological Engineering Urbana, IL, USA · University of Texas at El Paso Department of Computer Science El Paso, TX, USA · National Center for Supercomputing Applications Urbana, IL, USA

Industrial Control System Security Industrial Anomaly Detection Neuromorphic Computing Spiking Neural Networks Cybersecurity Online Continual Learning

Apr 13, 2026

SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving

Neural operators have emerged as powerful surrogates for partial differential equation (PDE) solvers, yet they are typically trained as monolithic models for individual PDEs, require energy-intensive GPU hardware, and must be retrained from scratch when new physics emerge. We introduce the Spiking Compositional Neural Operator (SCNO), a modular architecture combining spiking and conventional components that addresses all three limitations. SCNO maintains a library of small spiking neural operator blocks, each trained on a single elementary differential operator (convection, diffusion, reaction), and composes them through a lightweight input-conditioned aggregator to solve coupled PDEs not seen during block training. A small correction network learns cross-coupling residuals while keeping all blocks and the aggregator frozen, preserving zero-forgetting modular expansion by construction. We evaluate SCNO on eight PDE families including five coupled systems and a nuclear-relevant 1-group neutron diffusion equation. SCNO with correction achieves the lowest relative

L^2

error on four of five coupled PDEs, outperforming both a monolithic spiking DeepONet (by up to 62%, mean over 3 seeds) and a standard ANN DeepONet (by up to 65%), while requiring only 95K trainable parameters versus 462K for the monolithic baseline. To our knowledge, this is the first compositional spiking neural operator and the first proof-of-concept for modular neuromorphic PDE solving with built-in forgetting-free expansion.

Samrendra Roy, Souvik Chakraborty, Rizwan-uddin +1

University of Illinois Urbana-Champaign Department of Nuclear, Plasma & Radiological Engineering Urbana, IL, USA · Indian Institute of Technology Delhi Department of Applied Mechanics New Delhi, India · National Center for Supercomputing Applications Urbana, IL, USA

PDE Solving PDE Surrogate Modeling Neural Operators Modular Neural Networks Neuromorphic Computing

Apr 12, 2026

Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition

Wearable IMU-based Human Activity Recognition (HAR) relies heavily on Deep Neural Networks (DNNs), which are burdened by immense computational and buffering demands. Their power-hungry floating-point operations and rigid requirement to process complete temporal windows severely cripple battery-constrained edge devices. While Spiking Neural Networks (SNNs) offer extreme event-driven energy efficiency, standard architectures struggle with complex biomechanical topologies and temporal gradient degradation. To bridge this gap, we propose the Physics-Aware Spiking Neural Network (PAS-Net), a fully multiplier-free architecture explicitly tailored for Green HAR. Spatially, an adaptive symmetric topology mixer enforces human-joint physical constraints. Temporally, an

O(1)

-memory causal neuromodulator yields context-aware dynamic threshold neurons, adapting actively to non-stationary movement rhythms. Furthermore, we leverage a temporal spike error objective to unlock a flexible early-exit mechanism for continuous IMU streams. Evaluated across seven diverse datasets, PAS-Net achieves state-of-the-art accuracy while replacing dense operations with sparse 0.1 pJ integer accumulations. Crucially, its confidence-driven early-exit capability drastically reduces dynamic energy consumption by up to 98%. PAS-Net establishes a robust, ultra-low-power neuromorphic standard for always-on wearable sensing. The source code and pre-trained models are publicly available at https://github.com/zhengnaichuan2022/PAS-Net.git.

Naichuan Zheng, Hailun Xia, Zepeng Sun +2

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China · International School, Beijing University of Posts and Telecommunications, Beijing, China

Spiking Neural Networks Human Activity Recognition Efficient Neural Network Inference Wearable Sensing Neuromorphic Computing

Apr 11, 2026

Full Feature Spiking Neural Network Simulation on Micro-Controllers for Neuromorphic Applications at the Edge

Microcontroller units (MCU), which have an order of magnitude lower Size, Weight and Power (SWaP) than standard computers, makes them suitable for applications at the edge. Neuromorphic computing, which can realize low SWaP, relies on Spiking Neural Networks (SNNs). Until now, software based simulations of SNNs required GPU-based workstations, application classified core processors such as the ARM Cortex-A53, or specialized hardware like Intel's Loihi. In the present work, we demonstrate that the SNN simulator CARLsim can run its full feature set on a MCU RP2350 with 8 MB memory. We accomplished this by utilizing IEEE 16-bit float point numbers, which reduced memory requirements without loss of function. We were able to run the Synfire4 benchmark which comprises 1200 neurons. The accuracy was 97.5% compared to the standard single precision numbers. Furthermore, we show that CARLsim runs a Synfire4 benchmark scaled-down to 186 neurons on a MCU in real-time at only 20 mW. Compared to the smallest application class ARM processor used by Raspberry in their Pi Zero 2 W, our MCU implementation is five times more energy efficient for the SNN itself, and an order of magnitude better when compared to the complete SoC (MCU/CPU + Board).

L. Niedermeier, J. L. Krichmar

Niedermeier Consulting, Zurich, ZH, Switzerland · Department of Computer Science, University of California, Irvine, CA, USA · Department of Cognitive Sciences, University of California, Irvine, CA, USA

Neuromorphic Computing Spiking Neural Networks Edge Computing

Apr 10, 2026

Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing

Physical implementations of neural computation now extend far beyond silicon hardware, encompassing substrates such as memristive devices, photonic circuits, mechanical metamaterials, microfluidic networks, chemical reaction systems, and living neural tissue. By exploiting intrinsic physical processes such as charge transport, wave interference, elastic deformation, mass transport, and biochemical regulation, these substrates can realize neural inference and adaptation directly in matter. As silicon GPU-centered AI faces growing energy and data-movement constraints, physical neural computation is becoming increasingly relevant as a complementary path beyond conventional digital accelerators. This trend is driven in particular by pervasive intelligence, i.e., the deployment of on-device and edge AI across large numbers of resource-constrained systems. In such settings, co-locating computation with sensing and memory can reduce data shuttling and improve efficiency. Meanwhile, physical neural approaches have emerged across disparate disciplines, yet progress remains fragmented, with limited shared terminology and few principled ways to compare platforms. This survey unifies the field by mapping neural primitives to substrate-specific mechanisms, analyzing architectural and training paradigms, and identifying key engineering constraints including scalability, precision, programmability, and I/O interfacing overhead. To enable cross-domain comparison, we introduce a first-order benchmarking scheme based on standardized static and dynamic tasks and physically interpretable performance dimensions. We show that no single substrate dominates across the considered dimensions; instead, physical neural systems occupy complementary operating regimes, enabling applications ranging from ultrafast signal processing and in-memory inference to embodied control and in-sample biochemical decision making.

Stefan Fischer, Nihat Ay, Olaf Landsiedel +4

Institute for Telematics, University of Lübeck, Germany · Institute for Data Science Foundations, TU Hamburg, Germany · Institute for Networked Cyber-Physical Systems, TU Hamburg, Germany +6

Neuromorphic Computing Compute-in-Memory

Apr 10, 2026

A 0.5-V Linear Neuromorphic Voltage-to-Spike Encoder Using a Bulk-Driven Transconductor

This work introduces an ultralow-power voltage-to-spike encoder that achieves near-linear voltage-to-firing-rate conversion by pairing a linearized bulk-driven transconductor with a DPI-based LIF neuron. A tail-less bulk-driven differential pair improves large-signal linearity, while a translinear linearization network suppresses the dominant sinh nonlinearity and stabilizes the bias-tunable V-to-I gain. The resulting current feeds a DPI front-end that linearizes current-to-spike conversion. Fabricated in TSMC 0.18-um CMOS and operating at VDD = 0.5 V with 2-27 nA reference current, the encoder achieves a deviation of less than 5.6 percent from linearity over 0.1-0.4 V input, consumes 22-180 nW, and occupies 0.0074 mm^2.

Meysam Akbari, Erika Covi, Kea-Tiong Tang

ZIAM & CogniGron, University of Groningen, 9747 AG Groningen, The Netherlands · Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan

LIF Neurons Spike-Based Signal Encoding Neuromorphic Computing Neural Encoding Spiking Neural Networks

Apr 10, 2026

Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data

Damage caused by bushfires and volcanic eruptions escalates rapidly when detection is delayed, making fast and reliable early warning capabilities essential. Recent Earth Observation (EO) approaches have shown that thermal anomaly detection can be performed directly on decompressed Level-0 (L0) sensor data, avoiding computationally expensive preprocessing chains. However, direct exploitation of raw data remains challenging due to domain shift, sensor drift, radiometric inconsistencies, and the scarcity of labelled training samples. To address these challenges, this work proposes a Physics-Aware Neuromorphic Network (PANN) framework for onboard thermal anomaly detection. The proposed lightweight architecture, inspired by physical neural network principles and neuromorphic computing paradigms, is evaluated using two Sentinel-2 datasets: decompressed L0 with additional metadata (i.e. raw) and Level-1C (L1C). The PANN achieves a Matthews Correlation Coefficient (MCC) of

0.809

on raw measurements, compared to

0.875

when using ground-processed L1C products. The mean processing latency per L0 granule is

2.44 \pm 0.09~\mathrm{s}

, which is below the Sentinel-2 acquisition time of

3.6~\mathrm{s}

, demonstrating the feasibility of real-time, onboard processing. Furthermore, the projected execution time for the corresponding neuromorphic hardware instantiation is substantially lower at

0.1290 \pm 0.0002~\mathrm{s}

. Memory usage, including all necessary programs and packages, remains within realistic onboard constraints, with requirements of

0.673 \pm 0.007~\mathrm{Gb}

for the software PANN and

0.393 \pm 0.004~\mathrm{Gb}

for the estimated hardware realisation. Overall, these results indicate that PANN offers a promising pathway toward low-latency and resource-efficient onboard EO processing for thermal event detection.

Stephen Smith, Cormac Purcell, Gabriele Meoni +2

School of Physics, University of Sydney, Sydney, Australia · BolgianTen Ltd., Liverpool, United Kingdom · School of Computer Science, University of New South Wales, Sydney, Australia +3

Anomaly Detection Physics-Informed ML Neuromorphic Computing Wildfire Detection Efficient Neural Network Inference

Apr 9, 2026

Memory Wall is not gone: A Critical Outlook on Memory Architecture in Digital Neuromorphic Computing

The rapid advancement of neuromorphic technology aims to address the memory wall challenge inherent in conventional von Neumann architectures. This paper critically examines current digital neuromorphic processors and their strategies to mitigate this bottleneck. While designed to bring computation closer to memory through distributed architectures, our findings indicate that on-chip memory systems, including SRAM and emerging technologies like STT-MRAM, have become significant consumers of area and energy, leading to a new memory wall. Through an analysis of energy and area efficiency in various memory technologies, we argue that without a re-evaluation of memory organization, digital neuromorphic processors may struggle to compete effectively in edge and embedded applications. We conclude with potential pathways for future research to overcome the limitations of on-chip memory in neuromorphic systems.

Amirreza Yousefzadeh, Sameed Sohail, Ana Lucia Varbanescu

University of Twente, Enschede, NL

Neuromorphic Computing

Apr 6, 2026

Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization

Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advances encompass a wide range of conceptual, mathematical, and computational ideas, with applications for model learning and training, memory retrieval, data-driven control, and optimization. This tutorial focuses on neuro-inspired approaches to computation that aim to improve scalability, robustness, and energy efficiency across such tasks, bridging the gap between artificial and biological systems. Particular emphasis is placed on energy-based dynamical models that encode information through gradient flows and energy landscapes. We begin by reviewing classical formulations, such as continuous-time Hopfield networks and Boltzmann machines, and then extend the framework to modern developments. These include dense associative memory models for high-capacity storage, oscillator-based networks for large-scale optimization, and proximal-descent dynamics for composite and constrained reconstruction. The tutorial demonstrates how control-theoretic principles can guide the design of next-generation neurocomputing systems, steering the discussion beyond conventional feedforward and backpropagation-based approaches to artificial intelligence.

Arthur N. Montanari, Francesco Bullo, Dmitry Krotov +1

Center for Network Dynamics and the Department of Physics & Astronomy, Northwestern University, Evanston, IL 60208, USA · Center for Control, Dynamical Systems, and Computation and the Department of Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, USA · Independent Researcher, Cambridge, MA 02141, USA +1

Energy-Based Models Neural Network Optimization Neuromorphic Computing Associative Memory Hopfield Networks

Apr 6, 2026

Neuromorphic Computing for Low-Power Artificial Intelligence

Classical computing is beginning to encounter fundamental limits of energy efficiency. This presents a challenge that can no longer be solved by strategies such as increasing circuit density or refining standard semiconductor processes. The growing computational and memory demands of artificial intelligence (AI) require disruptive innovation in how information is represented, stored, communicated, and processed. By leveraging novel device modalities and compute-in-memory (CIM), in addition to analog dynamics and sparse communication inspired by the brain, neuromorphic computing offers a promising path toward improvements in the energy efficiency and scalability of current AI systems. But realizing this potential is not a matter of replacing one chip with another; rather, it requires a co-design effort, spanning new materials and non-volatile device structures, novel mixed-signal circuits and architectures, and learning algorithms tailored to the physics of these substrates. This article surveys the key limitations of classical complementary metal-oxide-semiconductor (CMOS) technology and outlines how such cross-layer neuromorphic approaches may overcome them.

Keshava Katti, Pratik Chaudhari, Deep Jariwala

Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, USA.

Analog Computing Neuromorphic Computing Energy-Efficient ML Compute-in-Memory

Apr 5, 2026

Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware

Reliable relative pose estimation is a key enabler for autonomous rendezvous and proximity operations, yet space imagery is notoriously challenging due to extreme illumination, high contrast, and fast target motion. Event cameras provide asynchronous, change-driven measurements that can remain informative when frame-based imagery saturates or blurs, while neuromorphic processors can exploit sparse activations for low-latency, energy-efficient inferences. This paper presents a spacecraft 6-DoF pose-estimation pipeline that couples event-based vision with the BrainChip Akida neuromorphic processor. Using the SPADES dataset, we train compact MobileNet-style keypoint regression networks on lightweight event-frame representations, apply quantization-aware training (8/4-bit), and convert the models to Akida-compatible spiking neural networks. We benchmark three event representations and demonstrate real-time, low-power inference on Akida V1 hardware. We additionally design a heatmap-based model targeting Akida V2 and evaluate it on Akida Cloud, yielding improved pose accuracy. To our knowledge, this is the first end-to-end demonstration of spacecraft pose estimation running on Akida hardware, highlighting a practical route to low-latency, low-power perception for future autonomous space missions.

Arunkumar Rathinam, Jules Lecomte, Jost Reelsen +3

University of Luxembourg · Fortiss GmbH, Munich, Germany · Technical University of Munich, Munich, Germany +1

Spacecraft Pose Estimation Relative Pose Estimation Event-Based Vision Neuromorphic Computing

Apr 3, 2026

YANA: Bridging the Neuromorphic Simulation-to-Hardware Gap

Spiking Neural Networks (SNNs) promise significant advantages over conventional Artificial Neural Networks (ANNs) for applications requiring real-time processing of temporally sparse data streams under strict power constraints -- a concept known as the Neuromorphic Advantage. However, the limited availability of neuromorphic hardware creates a substantial simulation-to-hardware gap that impedes algorithmic innovation, hardware-software co-design, and the development of mature open-source ecosystems. To address this challenge, we introduce Yet Another Neuromorphic Accelerator (YANA), an FPGA-based digital SNN accelerator designed to bridge this gap by providing an accessible hardware and software framework for neuromorphic computing. YANA implements a five-stage, event-driven processing pipeline that fully exploits temporal and spatial sparsity while supporting arbitrary SNN topologies through point-to-point neuron connections. The architecture features an input preprocessing scheme that maintains steady event processing at one event per cycle without buffer overflow risks, and implements hardware-efficient event-driven neuron updates using lookup tables for leak calculations. We demonstrate YANA's sparsity exploitation capabilities through experiments on the Spiking Heidelberg Digits dataset, showing near-linear scaling of inference time with both spatial and temporal sparsity levels. Deployed on the accessible AMD Kria KR260 platform, a single YANA core utilizes 740 LUTs, 918 registers, 7 BRAMS and 24 URAMs, supporting up to

2^{17}

synapses and

2^{10}

neurons. We release the YANA framework as an open-source project, providing an end-to-end solution for training, optimizing, and deploying SNNs that integrates with existing neuromorphic computing tools through the Neuromorphic Intermediate Representation (NIR).

Brian Pachideh, Sven Nitzsche, Moritz Neher +5

FZI Research Center for Information Technology, Karlsruhe, Germany · Karlsruhe Institute of Technology, Karlsruhe, Germany · Infineon Technologies, Dresden, Germany

Neuromorphic Computing Spiking Neural Networks FPGA-Based Neural Network Acceleration

Apr 3, 2026

Biologically Realistic Dynamics for Nonlinear Classification in CMOS+X Neurons

Spiking neural networks encode information in spike timing and offer a pathway toward energy efficient artificial intelligence. However, a key challenge in spiking neural networks is realizing nonlinear and expressive computation in compact, energy-efficient hardware without relying on additional circuit complexity. In this work, we examine nonlinear computation in a CMOS+X spiking neuron implemented with a magnetic tunnel junction connected in series with an NMOS transistor. Circuit simulations of a multilayer network solving the XOR classification problem show that three intrinsic neuronal properties enable nonlinear behavior: threshold activation, response latency, and absolute refraction. Threshold activation determines which neurons participate in computation, response latency shifts spike timing, and absolute refraction suppresses subsequent spikes. These results show that magnetization dynamics of MTJ devices can support nonlinear computation in compact neuromorphic hardware.

Steven Louis, Hannah Bradley, Artem Litvinenko +3

Department of Electrical and Computer Engineering · Oakland University, Rochester, MI USA · Department of Physics

Spiking Neural Networks Neuromorphic Computing

Apr 2, 2026

Computing with Living Neurons: Chaos-Controlled Reservoir Computing with Knowledge Transplant

We introduce chaos-controlled Reservoir Computing (cc-RC) for living neural cultures: dynamically rich substrates of unique potential for adaptive computation. To account for intrinsic biological variability, cc-RC combines: (i) pre-training identification of each culture's dynamical signature and phase-portrait attractor; (ii) low-power optical chaos control to stabilize spontaneous and stimulus-evoked activity; (iii) readout training within this controlled regime. Across hundreds of neural samples, cc-RC enables robust learning and pattern classification, improving both accuracy and model longevity by approximately 300% over standard RC. We further propose Knowledge Transplant (KT), for which the reservoir map learned by an expert culture is transplanted to an attractor-equivalent student culture, reducing training time to minutes while improving performance. By enabling cross-substrate, reusable learned models, KT paves the way for knowledge accumulation and sharing across neural populations, transcending biological lifespan limits.

Seung Hyun Kim, Zhi Dou, Gaurav Upadhyay +6

Mechanical Science and Engineering, University of Illinois Urbana-Champaign · Electrical and Computer Engineering, University of Illinois Urbana-Champaign · Coordinated Science Laboratory, University of Illinois Urbana-Champaign +6

Reservoir Computing Neuromorphic Computing Transfer Learning Chaotic Dynamical Systems

Apr 1, 2026

Oscillator-Based Associative Memory with Exponential Capacity: Theory, Algorithms, and Hardware Implementation

Associative memory systems enable content-addressable storage and retrieval of patterns, a capability central to biological neural computation and artificial intelligence. Classical implementations such as Hopfield networks face fundamental limitations in memory capacity, scaling at most linearly with network size. We present an associative memory architecture based on Kuramoto oscillator networks with honeycomb topology in which memories are encoded as stable phase-locked configurations. The honeycomb network consists of multiple cycles that share nodes in a chain-like arrangement, creating a one-dimensional lattice of chained+loops. We prove that this architecture achieves exponential memory capacity: a network of

N

oscillators can store

(2\lceil n_c/4 \rceil - 1)^m

distinct patterns, where

m

honeycomb cycles each contain

n_c

oscillators. Moreover, we fully characterize all stable configurations and prove that each memory's basin of attraction maintains a guaranteed minimum size independent of network scale. Simulations using charge-density-wave (CDW) oscillators validate predicted phase-locking behavior, demonstrating practical realizability in neuromorphic hardware.

Arie Ogranovich, Taosha Guo, Arvind R. Venkatakrishnan +3

Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, 93106, USA · Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, 92697, USA

Kuramoto Model Associative Memory Oscillatory Neural Networks Dynamical Systems Neuromorphic Computing