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：神经形态计算领域通过遵循类脑原理在提升计算效率与能力方面展现出巨大潜力。然而，神经形态研究中采用的技术多样性导致缺乏清晰的基准测试标准，阻碍了神经形态方法相较于传统深度学习方法优势与强项的有效评估。本文呈现一项学术界与工业界成员共同参与的合作性工作，旨在为神经形态计算定义基准测试：NeuroBench。NeuroBench的目标是由社区开发、服务于社区的协作性、公平性和代表性的基准测试套件。本文讨论了神经形态解决方案基准测试面临的挑战，并概述了NeuroBench的关键特性。我们相信NeuroBench将成为定义统一神经形态计算目标并推动其技术进步的里程碑。敬请访问neurobench.ai获取基准测试任务与指标的最新动态。