Exploration of task-based scheduling for convolutional neural networks accelerators under memory constraints
Crefeda Faviola Rodrigues, Graham D. Riley, Mikel Luján · 2019
Development of application specific accelerators for deep convolutional neural networks (ConvNets) have mainly focussed on accelerating the computationally intensive layers, that is the convolutional layers, to improve performance and energy efficiency. Traditional approaches in this space have relied on handcrafted dataflow implementations to leverage the fine-grained parallelism and data-locality properties within these layers. However, ConvNets layers also have an untapped potential from cross-layer data locality.