The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics.

翻译：神经形态计算领域在按照仿生学原理开展相关工作时具有极大的潜力，可以提高计算效率和性能。然而，该领域内众多技术的使用导致缺乏明确的标准来对相关技术进行基准测试，因此无法有效地评估神经形态计算方法与传统深度学习方法的优势和潜力。本文提出了一个协作计划，聚集了来自学术界和产业界的成员，致力于定义神经形态计算的基准测试：神经基准（NeuroBench）。神经基准的目标是成为由社区开发、面向社区的一套协作、公平和代表性的基准测试套件。在本文中，我们讨论关于进行神经形态解决方案基准测试所面临的挑战，并概述神经基准的关键特征。我们认为，神经基准将是定义统一神经形态计算目标并推动其技术进步的重要一步。请访问neurobench.ai获取基准任务和指标的最新更新。