Smart Hardware Implementation of Spiking Neural Networks
| Autor: | Fabio Galan-Prado, Josep L. Rosselló |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Spiking neural network
Flexibility (engineering) Quantitative Biology::Neurons and Cognition Exploit Artificial neural network business.industry Computer science Reliability (computer networking) Computer Science::Neural and Evolutionary Computation 02 engineering and technology computer.software_genre 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Computer Aided Design 020201 artificial intelligence & image processing business Field-programmable gate array Implementation computer 030217 neurology & neurosurgery Computer hardware |
| Zdroj: | Advances in Computational Intelligence ISBN: 9783319591520 IWANN (1) |
| DOI: | 10.1007/978-3-319-59153-7_48 |
| Popis: | During last years a lot of attention have been focused to the hardware implementation of Artificial Neural Networks (ANN) to efficiently exploit the inherent parallelism associated to these systems. From the different types of ANN, the Spiking Neural Networks (SNN) arise as a promising bio-inspired model that is able to emulate the expected neural behavior with a high confidence. Many works are centered in using analog circuitry to reproduce SNN with a high degree of precision, while minimizing the area and the energy costs. Nevertheless, the reliability and flexibility of these systems is lower if compared with digital implementations. In this paper we present a new, low-cost bio-inspired digital neural model for SNN along with an auxiliary Computer Aided Design (CAD) tool for the efficient implementation of high-volume SNN. |
| Databáze: | OpenAIRE |
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