Recursive Binary Neural Network Training Model for Efficient Usage of On-Chip Memory.

We present a novel deep learning model for a neural network that reduces both computation and data storage overhead. To do so, the proposed model proposes and combines a binary-weight neural network (BNN) training, a storage reuse technique, and an incremental training scheme. The storage requirements can be tuned to meet the desired classification accuracy, storing more parameters on an on-chip memory, and thereby reducing off-chip data storage accesses. Our experiments show 4–6 $\times $ reduction in weight storage footprint when training binary deep neural network models. On the FPGA platform, this results in a reduced amount of off-chip accesses, enabling our model to train a neural network in $14\times $ shorter latency, as compared to the conventional BNN training method. [ABSTRACT FROM AUTHOR]