Automatic generation of LUTs for Hardware Neural Networks

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• 作者：Mauricio A. Dias ; ^{macdias@icmc.usp.br" class="auth_mail" title="E-mail the corresponding author} ; ^{macdiaspae@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Author Vitae ; Daniel O. Sales ^{dsales@icmc.usp.br" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Fernando S. Osorio ^{fosorio@icmc.usp.br" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：LUT ; Hardware Neural Networks ; Automatic Generation and Activation Function
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 March 2016
• 年：2016
• 卷：180
• 期：Complete
• 页码：108-120
• 全文大小：2237 K

文摘

One very important component of Hardware implementations of Artificial Neural Networks (HNN) is the activation function. A common solution for activation function design in HNN is the Look-Up Table (LUT) representation that has acceptable area constraints and usually achieves acceptable execution time. Tools and techniques for automatic generation of LUTs have been developed but have some problems as high complexity, high dependency of external tools or languages and the optimization techniques that are not efficient enough for critical applications. In order to solve some of the presented problems this paper describes a method for automatic LUT generation that overcomes some deficiencies of existing methods using a simple approach. This method was evaluated initially considering the xor function and the sine function for evaluation purposes. After these initial tests, two other ANN based robotic systems were tested. These robotic systems will be implemented in hardware in the near future and they demonstrate a different behavior using different domains of the same activation function. Proposed method achieved good results that are directly related to how neural networks define the activation function domain.