A Prospect of Achieving Artificial Neural Networks through FPGA

With the beginning of the 21st century, industries and researchers witnessed the end of the Moore's Law. The stagnation of the size of transistor and other related limitations have compelled researchers to look for alternative approaches in order to maintain the momentum for design of computational intensive, high performing reliable systems focusing applications involving cloud computing, artificial intelligence, big data and IoT. The fuzzy Artificial Neural Network (ANN) based design approach have assisted a lot, in optimizing the systems manifold, for instance optimizing the system design targeting the pattern recognition problem in a large dataset. The capabilities and advantages associated with ANN have made it possible to address varied set of problems. Until now it was being implemented through software. Recent studies have showcased different limitations associated with software level ANN implementations. To address these issues hardware implementation of ANN based algorithms have been proposed and found more efficient vis-a-vis software based implementations. With the advances in technology, hardware available today is more efficient and capable. Devices based on GPUs, FPGAs and ASICs are used for the same, however FPGA shows more potentials as it is closer to wafer processing, providing options for high degree optimizations in power, speed and area; which seems to be perfect for today's dynamic dataset requirements. This paper focuses on the use of FPGAs for ANN inspired hardware level implementations. Authors have made a comprehensive study of the amalgamations of the technologies. Authors then propound the implementation of processing blocks, activations functions, weight managements and applications on the FPGA augmenting with external data interface.