Flow mapping and data distribution on mesh-based deep learning accelerator
Seyedeh Yasaman Hosseini Mirmahaleh, Midia Reshadi, Hesam Shabani, Xiaochen Guo, Nader Bagherzadeh · 2019
Convolutional neural networks have been proposed as an approach for classifying data corresponding to labeled and unlabeled datasets. The fast-growing data empowers deep learning algorithms to achieve higher accuracy. Numerous trained models have been proposed, which involve complex algorithms and increasing network depth. The main challenges of implementing deep convolutional neural networks are high energy consumption, high on-chip and off-chip bandwidth requirements, and large memory footprint. Different types of on-chip communication networks and traffic distribution methods have been proposed to reduce memory access latency and energy consumption of data movement. This paper proposes a new traffic distribution mechanism on a mesh topology using distributer nodes by considering memory access mechanism in the AlexNet, VggNet, and GoogleNet trained models. We also propose a flow mapping method (FMM) based on dataflow stationary which reduces energy consumption by 8%.