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Neural networks and SNN have been proved to be powerful tools for real world applications/tasks, such as pattern recognition, classification, regression, and prediction. However, simulating a large network in real-time requires high-performance machines or accelerators. Typical accelerators for large-scale NN accelerators use GPUs or ASIC chips. While ASICs deliver high performance, they lack the flexibility to reconfigure and hence are unable to adapt variation in the design and models employed. GPUs have better speedup over multi-core CPUs and good flexibility, but it lacks scalability to handle larger networks.
The goal of this research is to implement an FPGA-based Convolutional Neural Network for Pig Recognition.
