DianNao
International Centre for Theoretical Physics Asia-Pacific · Inria Saclay - Île de France
Abstract
Machine-Learning tasks are becoming pervasive in a broad range of domains, and in a broad range of systems (from embedded systems to data centers). At the same time, a small set of machine-learning algorithms (especially Convolutional and Deep Neural Networks, i.e., CNNs and DNNs) are proving to be state-of-the-art across many applications. As architectures evolve towards heterogeneous multi-cores composed of a mix of cores and accelerators, a machine-learning accelerator can achieve the rare combination of efficiency (due to the small number of target algorithms) and broad application scope.
Citation impact
- FWCI
- 85.02
- Percentile
- 100%
- References
- 54
Authors
7- TCTianshi ChenCorresponding
International Centre for Theoretical Physics Asia-Pacific
- ZDZidong Du
International Centre for Theoretical Physics Asia-Pacific
- NSNinghui Sun
International Centre for Theoretical Physics Asia-Pacific
- JWJia Wang
International Centre for Theoretical Physics Asia-Pacific
- CWChengyong Wu
International Centre for Theoretical Physics Asia-Pacific
Topics & keywords
- Computer science
- Convolutional neural network
- Scope (computer science)
- Deep learning
- Range (aeronautics)
- Artificial intelligence
- Set (abstract data type)
- Deep neural networks
- Affordable and clean energy