DaDianNao: A Neural Network Supercomputer
文献类型：期刊论文
| 作者 | Luo, Tao3; Liu, Shaoli3; Li, Ling1; Wang, Yuqing3; Zhang, Shijin3; Chen, Tianshi3; Xu, Zhiwei3; Temam, Olivier2; Chen, Yunji3 |
| 刊名 | IEEE TRANSACTIONS ON COMPUTERS
 |
| 出版日期 | 2017 |
| 卷号 | 66期号:1页码:73-88 |
| 关键词 | Machine learning neuron network supercomputer multi-chip interconnect CNN DNN |
| ISSN号 | 0018-9340 |
| DOI | 10.1109/TC.2016.2574353 |
| 英文摘要 | Many companies are deploying services largely based on machine-learning algorithms for sophisticated processing of large amounts of data, either for consumers or industry. The state-of-the-art and most popular such machine-learning algorithms are Convolutional and Deep Neural Networks (CNNs and DNNs), which are known to be computationally and memory intensive. A number of neural network accelerators have been recently proposed which can offer high computational capacity/area ratio, but which remain hampered by memory accesses. However, unlike the memory wall faced by processors on general-purpose workloads, the CNNs and DNNs memory footprint, while large, is not beyond the capability of the on-chip storage of a multi-chip system. This property, combined with the CNN/DNN algorithmic characteristics, can lead to high internal bandwidth and low external communications, which can in turn enable high-degree parallelism at a reasonable area cost. In this article, we introduce a custom multi-chip machine-learning architecture along those lines, and evaluate performance by integrating electrical and optical inter-chip interconnects separately. We show that, on a subset of the largest known neural network layers, it is possible to achieve a speedup of 656.63 x over a GPU, and reduce the energy by 184. 05 x on average for a 64-chip system. We implement the node down to the place and route at 28 nm, containing a combination of custom storage and computational units, with electrical inter-chip interconnects. |
| 资助项目 | NSF of China[61133004] ; NSF of China[61303158] ; NSF of China[61432016] ; NSF of China[61472396] ; NSF of China[61473275] ; NSF of China[61522211] ; NSF of China[61532016] ; NSF of China[61521092] ; 973 Program of China[2015CB358800] ; Strategic Priority Research Program of the CAS[XDA06010403] ; Strategic Priority Research Program of the CAS[XDB02040009] ; International Collaboration Key Program of the CAS[171111KYSB20130002] ; 10,000 talent program |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000390667600009 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/7753]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Luo, Tao |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Inria Scalay, F-91120 Palaiseau, France 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Tao,Liu, Shaoli,Li, Ling,et al. DaDianNao: A Neural Network Supercomputer[J]. IEEE TRANSACTIONS ON COMPUTERS,2017,66(1):73-88. |
| APA | Luo, Tao.,Liu, Shaoli.,Li, Ling.,Wang, Yuqing.,Zhang, Shijin.,...&Chen, Yunji.(2017).DaDianNao: A Neural Network Supercomputer.IEEE TRANSACTIONS ON COMPUTERS,66(1),73-88. |
| MLA | Luo, Tao,et al."DaDianNao: A Neural Network Supercomputer".IEEE TRANSACTIONS ON COMPUTERS 66.1(2017):73-88. |
入库方式： OAI收割
来源：计算技术研究所
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