Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Massachusetts Institute of Technology · Nvidia (United States)
Abstract
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of…
Citation impact
- FWCI
- 113.12
- Percentile
- 100%
- References
- 200
Authors
4Topics & keywords
- Computer science
- Benchmarking
- Key (lock)
- Field (mathematics)
- Computer architecture
- Artificial intelligence
- Software deployment
- Computer engineering
- Affordable and clean energy