Reconfigurable MAC-Based Architecture for Parallel Hardware Implementation on FPGAs of Artificial Neural Networks
Artificial Neural Networks (ANNs) is a well known bio- inspired model that simulates human brain capabilities such as learning and generalization. ANNs consist of a number of interconnected processing units, wherein each unit performs a weighted sum followed by the evaluation of a given activation function. The involved computation has a tremendous impact on the implementation efficiency. Existing hardware implementations of ANNs attempt to speed up the computational process. However these implementations require a huge silicon area that makes it almost impossible to fit within the resources available on a state-of-the-art FPGAs. In this paper, we devise a hardware architecture for ANNs that takes advantage of the dedicated adder blocks, commonly called MACs to compute both the weighted sum and the activation function. The proposed architecture requires a reduced silicon area considering the fact that the MACs come for free as these are FPGA’s built-in cores. The hardware is as fast as existing ones as it is massively parallel. Besides, the proposed hardware can adjust itself on-the-fly to the user-defined topology of the neural network, with no extra configuration, which is a very nice characteristic in robot-like systems considering the possibility of the same hardware may be exploited in different tasks.
KeywordsNeural Network Output Function Hardware Implementation Hardware Architecture Silicon Area
Unable to display preview. Download preview PDF.
- 4.Moerland, P., Fiesler, E.: Neural Network Adaptation to Hardware Implementations. In: Fiesler, E., Beale, R. (eds.) Handbook of Neural Computation. Oxford, New York (1996)Google Scholar
- 5.Navabi, Z.: VHDL: Analysis and Modeling of Digital Systems, 2nd edn. McGraw Hill, New York (1998)Google Scholar
- 6.Nedjah, N., Mourelle, L.M.: Reconfigurable Hardware for Neural Networks: Binary radix vs. Stochastic. Journal of Neural Computing and Applications 16(3), 155–249 (2007)Google Scholar
- 7.Xilinx, Inc. Foundation Series Software, http://www.xilinx.com