Computing with Spikes: The Advantage of Fine-Grained Timing
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Neural-inspired spike-based computing machines often claim to achieve considerable advantages in terms of energy and time efficiency by using spikes for computation and communication. However, fundamental questions about spike-based computation remain unanswered. For instance, how much advantage do spike-based approaches have over conventional methods, and under what circumstances does spike-based computing provide a comparative advantage? Simply implementing existing algorithms using spikes as the medium of computation and communication is not guaranteed to yield an advantage. Here, we demonstrate that spike-based communication and computation within algorithms can increase throughput, and they can decrease energy cost in some cases. We present several spiking algorithms, including sorting a set of numbers in ascending/descending order, as well as finding the maximum or minimum or median of a set of numbers. We also provide an example application: a spiking median-filtering approach for image processing providing a low-energy, parallel implementation. Furthermore, the algorithms and analyses presented here demonstrate that spiking algorithms can provide performance advantages and offer efficient computation of fundamental operations useful in more complex algorithms.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1466763
- Report Number(s):
- SAND--2018-9039J; {"Journal ID: ISSN 0899-7667",667190}
- Journal Information:
- Neural Computation, Journal Name: Neural Computation Journal Issue: 10 Vol. 30; ISSN 0899-7667
- Publisher:
- MIT PressCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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