Cyclic Reservoir Computing with FPGA Devices for Efficient Channel Equalization
| Autor: | Miquel L. Alomar, Miquel Roca, Eugeni Isern, Erik S. Skibinsky-Gitlin, Christiam F. Frasser, Vincent Canals, Josep L. Rosselló |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry 020208 electrical & electronic engineering Reservoir computing Topology (electrical circuits) 02 engineering and technology Recurrent neural network Software Computer engineering Limit (music) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Field-programmable gate array RC circuit business Realization (systems) |
| Zdroj: | Artificial Intelligence and Soft Computing ISBN: 9783319912523 ICAISC (1) |
| DOI: | 10.1007/978-3-319-91253-0_22 |
| Popis: | The reservoir computation (RC) is a recurrent neural network architecture that is very suitable for time series prediction tasks. Its implementation in specific hardware can be very useful in relation to software approaches, especially when low consumption is an essential requirement. However, the hardware realization of RC systems is expensive in terms of circuit area and power dissipation, mainly due to the need of a large number of multipliers at the synapses. In this paper, we present an implementation of an RC network with cyclic topology (simple cyclic reservoir) in which we limit the available synapses’ weights, which makes it possible to replace the multiplications with simple addition operations. This design is evaluated to implement the equalization of a non-linear communication channel, and allows significant savings in terms of hardware resources, presenting an accuracy comparable to previous works. |
| Databáze: | OpenAIRE |
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