Dataflow Based

Dataflow-based architectures are revolutionizing the design of deep neural network (DNN) accelerators, aiming to improve energy efficiency and performance by optimizing data movement between processing units. Current research focuses on developing flexible dataflow designs adaptable to various DNN models, incorporating sparsity techniques to reduce computation and memory needs, and addressing challenges in efficiently handling specialized convolution types like dilated and transposed convolutions. These advancements are significant for deploying DNNs on resource-constrained devices like edge computers and mobile phones, enabling faster and more energy-efficient inference and training.