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.

本文介绍了神经形态计算中的基准测试 NeuroBench 及其目标，以促进该领域的技术进步和统一标准，该基准测试是由学术界和工业界的成员共同制定的。

NeuroBench: 通过合作、公平和代表性的基准测试推动神经形态计算

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair  and Representative Benchmarking

Progress in science has advanced the development of human society across
history, with dramatic revolutions shaped by information theory, genetic
cloning, and artificial intelligence, among the many scientific achievements
produced in the 20th century. However, the way that science advances itself is
much less well-understood. In this work, we study the evolution of scientific
development over the past century by presenting an anatomy of 89 million
digitalized papers published between 1900 and 2015. We find that science has
benefited from the shift from individual work to collaborative effort, with
over 90% of the world-leading innovations generated by collaborations in this
century, nearly four times higher than they were in the 1900s. We discover that
rather than the frequent myopic- and self-referencing that was common in the
early 20th century, modern scientists instead tend to look for literature
further back and farther around. Finally, we also observe the globalization of
scientific development from 1900 to 2015, including 25-fold and 7-fold
increases in international collaborations and citations, respectively, as well
as a dramatic decline in the dominant accumulation of citations by the US, the
UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are
meant to serve as a starter for exploring the visionary ways in which science
has developed throughout the past century, generating insight into and an
impact upon the current scientific innovations and funding policies.

研究了 1900 年至 2015 年间的科学发展历程，发现合作研究已成为创新的主要来源，广泛参考文献，全球化的合作和引用关系大幅上升，美国，英国和德国科学累积的引用数量下降，这些发现有助于更好地探索科学发展的未来。