Yanzhi Wang Awarded Best Paper at ICLR Workshop
ECE Assistant Professor Yanzhi Wang was awarded Best Paper titled, “Memory-Bounded Sparse Training on the Edge” at ICLR Workshop on Hardware-Aware Efficient Training (HAET).
The aim of the workshop is to reach top-tier performance, deep learning architectures usually rely on a large number of parameters and operations, and thus require to be processed using considerable power and memory. Numerous works have proposed to tackle this problem using quantization of parameters, pruning, clustering of parameters, decompositions of convolutions or using distillation. However, most of these works aim at accelerating only the inference process and disregard the training phase. In practice, however, it is the learning phase that is by far the most complex. There has been recent efforts in introducing some compression on training process, however it remains challenging.