The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning
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. Until now, most machine-learning accelerator designs have focused 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 3.02 mm2 and 485 mW; compared to a 128-bit 2GHz SIMD processor, the accelerator is 117.87x faster, and it can reduce the total energy by 21.08x. The accelerator characteristics are obtained after layout at 65 nm. 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.
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Cited by
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
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Deep Learning for IoT Big Data and Streaming Analytics: A Survey
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Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
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Self-citationA Survey on Distributed Machine Learning
Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2021) · ACM Computing Surveys · doi:10.1145/3377454 · Cited by 872
Caffeine: towards uniformed representation and acceleration for deep convolutional neural networks
Zhang, C., Fang, Z., Zhou, P., Pan, P., & Cong, J. (2016) · Proceedings of the 35th International Conference on Computer-Aided Design · doi:10.1145/2966986.2967011 · Cited by 299
Metadata
- Authors
- Chen, T., Du, Z., Sun, N., Wang, J., Wu, C., Chen, Y., & Temam, O.
- Venue
- ACM SIGPLAN Notices
- Published
- 2014-02-24
- Volume
- 49
- Issue
- 4
- Pages
- 269-284
- ISSN
- 0362-1340 (ISSN-L)
- 1558-1160
- OpenAlex
- W4212788319
- Cited by
- 281
- References
- 32
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, T., Du, Z., Sun, N., Wang, J., Wu, C., Chen, Y., & Temam, O. (2014). DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning. ACM SIGPLAN Notices. https://doi.org/10.1145/2644865.2541967
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