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EnglishThis paper presents a fully parametrized framework, entirely described in VHDL, to simplify the FPGA implementation of non-recurrent Artificial Neural Networks (ANNs), which works independently of the complexity of the networks in terms of number of neurons, layers and, to some extent, overall topology. More specifically, the network may consist of fully-connected, max-pooling or convolutional layers which can be arbitrarily combined. The ANN is used only for inference, while back-propagation is performed off-line during the ANN learning phase. Target of this work is to achieve fast-prototyping, small, low-power and cost-effective implementation of ANNs to be employed directly on the sensing nodes of IOT (i.e. Edge Computing). The performance of so-implemented ANNs is assessed for two real applications, namely hand movement recognition based on electromyographic signals and handwritten character recognition. Energy per operation is measured in the FPGA realization and compared with the corresponding ANN implemented on a microcontroller (μC) to demonstrate the advantage of the FPGA based solution.
A High-level Implementation Framework for Non-Recurrent Artificial Neural Networks on FPGA / PRONO, LUCIANO; Marchioni, A.; Mangia, M.; Pareschi, F.; Rovatti, R.; Setti, G.. - STAMPA. - 2019(2019), pp. 77-80. ((Intervento presentato al convegno 15th Conference on Ph.D. Research in Microelectronics and Electronics, PRIME 2019 tenutosi a Lausanne (Switzerland) nel July 15-18, 2019.
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| Titolo: | A High-level Implementation Framework for Non-Recurrent Artificial Neural Networks on FPGA |
| Autori: | |
| Data di pubblicazione: | 2019 |
| Abstract: | This paper presents a fully parametrized framework, entirely described in VHDL, to simplify the F...PGA implementation of non-recurrent Artificial Neural Networks (ANNs), which works independently of the complexity of the networks in terms of number of neurons, layers and, to some extent, overall topology. More specifically, the network may consist of fully-connected, max-pooling or convolutional layers which can be arbitrarily combined. The ANN is used only for inference, while back-propagation is performed off-line during the ANN learning phase. Target of this work is to achieve fast-prototyping, small, low-power and cost-effective implementation of ANNs to be employed directly on the sensing nodes of IOT (i.e. Edge Computing). The performance of so-implemented ANNs is assessed for two real applications, namely hand movement recognition based on electromyographic signals and handwritten character recognition. Energy per operation is measured in the FPGA realization and compared with the corresponding ANN implemented on a microcontroller (μC) to demonstrate the advantage of the FPGA based solution. |
| ISBN: | 978-1-7281-3549-6 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2786317
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