Abstract
Abstract
Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (‘cognitive’ applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.
Funder
National Nuclear Security Administration
U.S. Department of Energy
Reference118 articles.
1. A survey of neuromorphic computing and neural networks in hardware;Schuman,2017
2. Loihi: a neuromorphic manycore processor with on-chip learning;Davies;IEEE Micro,2018
3. Advancing neuromorphic computing with Loihi: a survey of results and outlook;Davies;Proc. IEEE,2021
4. A digital neurosynaptic core using embedded crossbar memory with 45 pJ per spike in 45 nm;Merolla,2011
5. TrueNorth: accelerating from zero to 64 million neurons in 10 years;DeBole;Computer,2019
Cited by
22 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献