# NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

arXiv: https://arxiv.org/abs/2304.04640 · HTML: https://mlsanity.com/paper/2304.04640/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 65 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 27 of 27)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2004.10956/) (2020)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks](https://mlsanity.com/paper/1910.07407/) (2019)
- [MLPerf Training Benchmark](https://mlsanity.com/paper/1910.01500/) (2019)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions](https://mlsanity.com/paper/2005.14623/) (2020)
- [An Analytical Estimation of Spiking Neural Networks Energy Efficiency](https://mlsanity.com/paper/2210.13107/) (2022)
- [Model scale versus domain knowledge in statistical forecasting of chaotic systems](https://mlsanity.com/paper/2303.08011/) (2023)
- [Parallelizing Legendre Memory Unit Training](https://mlsanity.com/paper/2102.11417/) (2021)
- [Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain](https://mlsanity.com/paper/1805.08932/) (2018)
- [Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence](https://mlsanity.com/paper/2106.01288/) (2021)
- [Progress in Mathematical Programming Solvers from 2001 to 2020](https://mlsanity.com/paper/2206.09787/) (2022)
- [Composing Neural Algorithms with Fugu](https://mlsanity.com/paper/1905.12130/) (2019)
- [Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural Networks](https://mlsanity.com/paper/2110.02865/) (2021)
- [Trends in Energy Estimates for Computing in AI/Machine Learning Accelerators, Supercomputers, and Compute-Intensive Applications](https://mlsanity.com/paper/2210.17331/) (2022)

## Built on by (most cited first, 27 of 27)

- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges](https://mlsanity.com/paper/2507.19672/) (2025)
- [Backpropagation through space, time, and the brain](https://mlsanity.com/paper/2403.16933/) (2024)
- [Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks](https://mlsanity.com/paper/2411.04760/) (2024)
- [Solving QUBO on the Loihi 2 Neuromorphic Processor](https://mlsanity.com/paper/2408.03076/) (2024)
- [State-Space Model Inspired Multiple-Input Multiple-Output Spiking Neurons](https://mlsanity.com/paper/2504.02591/) (2025)
- [Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces](https://mlsanity.com/paper/2312.15889/) (2023)
- [SNNAX -- Spiking Neural Networks in JAX](https://mlsanity.com/paper/2409.02842/) (2024)
- [Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone](https://mlsanity.com/paper/2504.10400/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models](https://mlsanity.com/paper/2405.00433/) (2024)
- [Decoding finger velocity from cortical spike trains with recurrent spiking neural networks](https://mlsanity.com/paper/2409.01762/) (2024)
- [Leveraging Recurrent Neural Networks for Predicting Motor Movements from Primate Motor Cortex Neural Recordings](https://mlsanity.com/paper/2410.22283/) (2024)
- [RGB-Event Fusion with Self-Attention for Collision Prediction](https://mlsanity.com/paper/2505.04258/) (2025)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback](https://mlsanity.com/paper/2508.06292/) (2025)
- [Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems](https://mlsanity.com/paper/2412.05126/) (2024)
- [STEMS: Spatial-Temporal Mapping For Spiking Neural Networks](https://mlsanity.com/paper/2502.03287/) (2025)
- [An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface](https://mlsanity.com/paper/2511.22108/) (2025)
- [L-SPINE: A Low-Precision SIMD Spiking Neural Compute Engine for Resource-efficient Edge Inference](https://mlsanity.com/paper/2604.03626/) (2026)
- [Multi-Timescale Conductance Spiking Networks: A Sparse, Gradient-Trainable Framework with Rich Firing Dynamics for Enhanced Temporal Processing](https://mlsanity.com/paper/2605.11835/) (2026)
- [NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework](https://mlsanity.com/paper/2605.15058/) (2026)
- [Time-Varying Deep State Space Models for Sequences with Switching Dynamics](https://mlsanity.com/paper/2605.15311/) (2026)
- [Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions](https://mlsanity.com/paper/2605.15355/) (2026)
- [Event-based Neural Decoding for Neuroprosthetic Motor Control](https://mlsanity.com/paper/2607.11445/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)
- [Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning](https://mlsanity.com/paper/2607.29353/) (2026)

## 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.