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- Title
An Approach of Feed-Forward Neural Network Throughput-Optimized Implementation in FPGA.
- Authors
Novickis, Rihards; Justs, Daniels Jānis; Ozols, Kaspars; Greitāns, Modris
- Abstract
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 (HLS) tools. Various topologies are implemented and benchmarked, and a comparison with related work is provided. The proposed methodology is applied for the implementation of high-throughput virtual sensor.
- Subjects
ARTIFICIAL neural networks; MANUFACTURING processes
- Publication
Electronics (2079-9292), 2020, Vol 9, Issue 12, p2193
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics9122193