Convolutional networks for fast, energy-efficient neuromorphic computing
Abstract
Brain-inspired computing seeks to develop new technologies that solve real-world problems while remaining grounded in the physical requirements of energy, speed, and size. Meeting these challenges requires high-performing algorithms that are capable of running on efficient hardware. Here, we adapt deep convolutional neural networks, which are today’s state-of-the-art approach for machine perception in many domains, to perform classification tasks on neuromorphic hardware, which is today’s most efficient platform for running neural networks. Using our approach, we demonstrate near state-of-the-art accuracy on eight datasets, while running at between 1,200 and 2,600 frames/s and using between 25 and 275 mW.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 20, 2016
- Source ID
- 10.1073/pnas.1604850113
Entities
People
- Alexander Andreopoulos
- Andrew S. Cassidy
- Arnon Amir
- Brian Taba
- Carmelo Di Nolfo
- David J. Berg
- Davis R. Barch
- Dharmendra S. Modha
- Jeffrey L. Mckinstry
- John V. Arthur
- Myron D. Flickner
- Pallab Datta
- Paul A. Merolla
- Rathinakumar Appuswamy
- Steven K. Esser
- Timothy Melano
Organizations
- International Business Machines Corporation (Armonk, NY)