Scalable and Sustainable Deep Learning via Randomized Hashing

TL;DR

This paper introduces a hashing-based method to significantly reduce the computational and energy costs of training and testing deep neural networks by selecting only the most active nodes, maintaining high accuracy.

Contribution

The paper presents a novel hashing technique that enables sparse, efficient deep learning with drastically fewer computations, suitable for parallel and energy-efficient environments.

Findings

Uses only 5% of multiplications of traditional models

Maintains within 1% accuracy of full models

Achieves near-linear speedup with more cores

Abstract

Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with both training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing based technique to drastically reduce the amount of computation needed to train and test deep networks. Our approach combines recent ideas from adaptive dropouts and randomized hashing for maximum inner product search to select the nodes with the highest activation efficiently. Our new algorithm for deep learning reduces the overall computational cost of forward and back-propagation by operating on significantly fewer (sparse) nodes. As a consequence, our algorithm uses only 5% of the total multiplications, while keeping on average within 1% of the accuracy of the original model. A unique property of the proposed hashing based back-propagation is that the updates are always sparse. Due to the sparse gradient updates, our algorithm is ideally suited for asynchronous and parallel training leading to near linear speedup with increasing number of cores. We demonstrate the scalability and sustainability (energy efficiency) of our proposed algorithm via rigorous experimental evaluations on several real datasets.