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Neuromorphic computing shows promise for advancing computing efficiency andcapabilities of AI applications using brain-inspired principles. However, theneuromorphic research field currently lacks standardized benchmarks, making itdifficult to accurately measure technological advancements, compare performancewith conventional methods, and identify promising future research directions.Prior neuromorphic computing benchmark efforts have not seen widespreadadoption due to a lack of inclusive, actionable, and iterative benchmark designand guidelines. To address these shortcomings, we present NeuroBench: abenchmark framework for neuromorphic computing algorithms and systems.NeuroBench is a collaboratively-designed effort from an open community ofnearly 100 co-authors across over 50 institutions in industry and academia,aiming to provide a representative structure for standardizing the evaluationof neuromorphic approaches. The NeuroBench framework introduces a common set oftools and systematic methodology for inclusive benchmark measurement,delivering an objective reference framework for quantifying neuromorphicapproaches in both hardware-independent (algorithm track) andhardware-dependent (system track) settings. In this article, we present initialperformance baselines across various model architectures on the algorithm trackand outline the system track benchmark tasks and guidelines. NeuroBench isintended to continually expand its benchmarks and features to foster and trackthe progress made by the research community.

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems