An Approach of Feed-Forward Neural Network Throughput-Optimized Implementation in FPGA”, Electronics journal: Special Issue Advanced AI Hardware Designs Based on FPGAs, 2020

All authors:

Rihards Novickis, Daniels Jānis Justs, Kaspars Ozols, Modris Greitāns


Artificial Neural Networks (ANNs) have become an accepted approach for a wide range of challenges. Meanwhile, the advancement of chip manufacturing processes is approaching saturation which calls for new computing solutions. This work presents a novel approach of an FPGA-based accelerator development for fully connected feed-forward neural networks (FFNNs). A specialized tool was developed to facilitate different implementations, which splits FFNN into elementary layers, allocates computational resources and generates high-level C++ description for high-level synthesis tools. Various topologies are implemented and benchmarked, and comparison with related work is provided. The proposed methodology is applied for the implementation of high-throughput virtual sensor.