Deep neural networks with rate-based neurons have exhibited tremendous progress in the last decade. However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data, energy-efficiency and low la-tency. This could be because the benchmarking techniques for SNNs are based on the methods used for evaluating deep neural networks, which do not provide a clear evaluation of the capabilities of SNNs. Particularly, the benchmarking of SNN approaches with regards to energy efficiency and latency requires realization in suitable hardware, which imposes additional temporal and resource constraints upon ongoing projects. This review aims to provide an overview of the current re-al-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future.
|Number of pages||12|
|Journal||Big Data and Cognitive Computing|
|Early online date||15 Nov 2021|
|Publication status||Published online - 15 Nov 2021|
Bibliographical notePublisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
- spiking neural networks
- neuromorphic computing
- brain-inspired learning
- Brain-inspired learning
- Neuromorphic computing
- Spiking neural networks