Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic
Convolutional Neural Networks have dramatically improved in recent years, surpassing human accuracy on certain problems and performance exceeding that of traditional computer vision algorithms. While the compute pattern in itself is relatively simple, significant compute and memory challenges remain as CNNs may contain millions of floating-point parameters and require billions …