Name
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 toward heterogeneous multicores 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. Until now, most machine-learning accelerator designs have been focusing on efficiently implementing the computational part of the algorithms. However, recent state-of-the-art CNNs and DNNs are characterized by their large size. In this study, we design an accelerator for large-scale CNNs and DNNs, with a special emphasis on the impact of memory on accelerator design, performance, and energy. We show that it is possible to design an accelerator with a high throughput, capable of performing 452 GOP/s (key NN operations such as synaptic weight multiplications and neurons outputs additions) in a small footprint of 3.02mm<sup>2</sup> and 485mW; compared to a 128-bit 2GHz SIMD processor, the accelerator is 117.87 × faster, and it can reduce the total energy by 21.08 ×. The accelerator characteristics are obtained after layout at 65nm. Such a high throughput in a small footprint can open up the usage of state-of-the-art machine-learning algorithms in a broad set of systems and for a broad set of applications. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2701417 | ACM Transactions on Computer Systems |
Keywords | Field | DocType |
Architecture,Processor,Hardware | Computer science,Convolutional neural network,Real-time computing,Footprint,Hardware acceleration,Artificial intelligence,Throughput,Deep learning,Artificial neural network,Small set,Synaptic weight,Distributed computing | Journal |
Volume | Issue | ISSN |
33 | 2 | 0734-2071 |
Citations | PageRank | References |
3 | 0.44 | 33 |
Authors | ||
12 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Tianshi | 1 | 1205 | 59.29 |
| Shijin Zhang | 2 | 406 | 16.44 |
| Shaoli Liu | 3 | 560 | 27.88 |
| Zidong Du | 4 | 574 | 29.68 |
| Tao Luo | 5 | 601 | 23.27 |
| Yuan Gao | 6 | 40 | 2.12 |
| Junjie Liu | 7 | 9 | 9.04 |
| Dongsheng Wang | 8 | 373 | 64.93 |
| Chengyong Wu | 9 | 515 | 26.67 |
| SUN Ning-Hui | 10 | 1268 | 97.37 |
| Yunji Chen | 11 | 1432 | 79.99 |
| Olivier Temam | 12 | 2474 | 148.79 |