mlsanityData to 7 Oct 2026

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

arXiv 2304.04640 ↗First posted 10 Apr 2023cs.AIcited by 27

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Abstract (arXiv)

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we outline tasks and guidelines for benchmarks across multiple application domains, and present initial performance baselines across neuromorphic and conventional approaches for both benchmark tracks. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 27 of its 65 references that are in the map. First posted on arXiv 10 Apr 2023.

Builds on

27 of 65 references.

References · 27

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15 more
  1. 2022Compute Trends Across Three Eras of Machine Learningcited by 161
  2. 2019The Heidelberg spiking datasets for the systematic evaluation of spiking neural networkscited by 135
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 9; 2025: 9; 2026: 8.

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  1. 2024Evaluating Human-AI Collaboration: A Review and Methodological Frameworkcited by 25
  2. 2025Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challengescited by 14
  3. 2024Backpropagation through space, time, and the braincited by 10
  4. 2024Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networkscited by 6
  5. 2024Solving QUBO on the Loihi 2 Neuromorphic Processorcited by 4
  6. 2025State-Space Model Inspired Multiple-Input Multiple-Output Spiking Neuronscited by 4
  7. 2023Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfacescited by 3
  8. 2024SNNAX -- Spiking Neural Networks in JAXcited by 3
  9. 2025Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Dronecited by 3
  10. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  11. 2024Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Modelscited by 1
  12. 2024Decoding finger velocity from cortical spike trains with recurrent spiking neural networkscited by 1
15 more
  1. 2024Leveraging Recurrent Neural Networks for Predicting Motor Movements from Primate Motor Cortex Neural Recordingscited by 1
  2. 2025RGB-Event Fusion with Self-Attention for Collision Predictioncited by 1
  3. 2025IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processingcited by 1
  4. 2025Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedbackcited by 1
  5. 2024Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systemscited by 0
  6. 2025STEMS: Spatial-Temporal Mapping For Spiking Neural Networkscited by 0
  7. 2025An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interfacecited by 0
  8. 2026L-SPINE: A Low-Precision SIMD Spiking Neural Compute Engine for Resource-efficient Edge Inferencecited by 0
  9. 2026Multi-Timescale Conductance Spiking Networks: A Sparse, Gradient-Trainable Framework with Rich Firing Dynamics for Enhanced Temporal Processingcited by 0
  10. 2026NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Frameworkcited by 0
  11. 2026Time-Varying Deep State Space Models for Sequences with Switching Dynamicscited by 0
  12. 2026Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutionscited by 0
  13. 2026Event-based Neural Decoding for Neuroprosthetic Motor Controlcited by 0
  14. 2026Neuromorphic Object Detection: An In-Depth Study and Future Directionscited by 0
  15. 2026Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learningcited by 0