Stochastic Neural Computation II: Soft Competitive Learning
IEEE Transactions on Computers
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Reconfigurable hardware for neural networks: binary versus stochastic
Neural Computing and Applications
Compact yet efficient hardware implementation of artificial neural networks with customized topology
Expert Systems with Applications: An International Journal
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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.