Accuracy, Training Time and Hardware Efficiency Trade-Offs for Quantized Neural Networks on FPGAs

Neural networks have proven a successful AI approach in many application areas. Some neural network deployments require low inference latency and lower power requirements to be useful e.g. autonomous vehicles and smart drones. Whilst FPGAs meet these requirements, hardware needs of neural networks to execute often exceed FPGA resources.