ADaPT

Abstract

Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted to extract important information to help advance healthcare, make our cities smarter, and innovate in smart home technology. Deep convolutional neural networks, which are at the heart of many emerging Internet-of-Things (IoT) applications, achieve remarkable performance in audio and visual recognition tasks, at the expense of high computational complexity in convolutional layers, limiting their deployability. In this paper, we present an easy-to-implement acceleration scheme, named ADaPT, which can be applied to already available pre-trained networks. Our proposed technique exploits redundancy present in the convolutional layers to reduce computation and storage requirements. Additionally, we also decompose each convolution layer into two consecutive one-dimensional stages to make full use of the approximate model. This technique can easily be applied to existing low power processors, GPUs or new accelerators. We evaluated this technique using four diverse and widely used benchmarks, on hardware ranging from embedded CPUs to server GPUs. Our experiments show an average 3-5x speed-up in all deep models and a maximum 8-9x speed-up on many individual convolutional layers. We demonstrate that unlike iterative pruning based methodology, our approximation technique is mathematically well grounded, robust, does not require any time-consuming retraining, and still achieves speed-ups solely from convolutional layers with no loss in baseline accuracy.