Softmax regression design for stochastic computing based deep convolutional neural networks

Zihao Yuan; Ji Li; Zhe Li; Caiwen Ding; Ao Ren; Bo Yuan; Qinru Qiu; Jeffrey Draper; Yanzhi Wang

doi:10.1145/3060403.3060467

Softmax regression design for stochastic computing based deep convolutional neural networks

Zihao Yuan, Ji Li, Zhe Li, Caiwen Ding, Ao Ren, Bo Yuan, Qinru Qiu, Jeffrey Draper, Yanzhi Wang

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

15 Scopus citations

Abstract

Recently, Deep Convolutional Neural Networks (DCNNs) have made tremendous advances, achieving close to or even better accuracy than human-level perception in various tasks. Stochastic Computing (SC), as an alternate to the conventional binary computing paradigm, has the potential to enable massively parallel and highly scalable hardware implementations of DCNNs. In this paper, we design and optimize the SC based Softmax Regression function. Experiment results show that compared with a binary SR, the proposed SC-SR under longer bit stream can reach the same level of accuracy with the improvement of 295X, 62X, 2617X in terms of power, area and energy, respectively. Binary SR is suggested for future DCNNs with short bit stream length input whereas SC-SR is recommended for longer bit stream.

Original language	English (US)
Title of host publication	GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017
Publisher	Association for Computing Machinery
Pages	467-470
Number of pages	4
Volume	Part F127756
ISBN (Electronic)	9781450349727
DOIs	https://doi.org/10.1145/3060403.3060467
State	Published - May 10 2017
Event	27th Great Lakes Symposium on VLSI, GLSVLSI 2017 - Banff, Canada Duration: May 10 2017 → May 12 2017

Other

Other	27th Great Lakes Symposium on VLSI, GLSVLSI 2017
Country/Territory	Canada
City	Banff
Period	5/10/17 → 5/12/17

ASJC Scopus subject areas

Engineering(all)

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10.1145/3060403.3060467

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Yuan, Z., Li, J., Li, Z., Ding, C., Ren, A., Yuan, B., Qiu, Q., Draper, J., & Wang, Y. (2017). Softmax regression design for stochastic computing based deep convolutional neural networks. In GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017 (Vol. Part F127756, pp. 467-470). Association for Computing Machinery. https://doi.org/10.1145/3060403.3060467

Yuan, Z, Li, J, Li, Z, Ding, C, Ren, A, Yuan, B, Qiu, Q, Draper, J & Wang, Y 2017, Softmax regression design for stochastic computing based deep convolutional neural networks. in GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017. vol. Part F127756, Association for Computing Machinery, pp. 467-470, 27th Great Lakes Symposium on VLSI, GLSVLSI 2017, Banff, Canada, 5/10/17. https://doi.org/10.1145/3060403.3060467

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title = "Softmax regression design for stochastic computing based deep convolutional neural networks",

abstract = "Recently, Deep Convolutional Neural Networks (DCNNs) have made tremendous advances, achieving close to or even better accuracy than human-level perception in various tasks. Stochastic Computing (SC), as an alternate to the conventional binary computing paradigm, has the potential to enable massively parallel and highly scalable hardware implementations of DCNNs. In this paper, we design and optimize the SC based Softmax Regression function. Experiment results show that compared with a binary SR, the proposed SC-SR under longer bit stream can reach the same level of accuracy with the improvement of 295X, 62X, 2617X in terms of power, area and energy, respectively. Binary SR is suggested for future DCNNs with short bit stream length input whereas SC-SR is recommended for longer bit stream.",

author = "Zihao Yuan and Ji Li and Zhe Li and Caiwen Ding and Ao Ren and Bo Yuan and Qinru Qiu and Jeffrey Draper and Yanzhi Wang",

year = "2017",

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doi = "10.1145/3060403.3060467",

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pages = "467--470",

booktitle = "GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017",

publisher = "Association for Computing Machinery",

note = "27th Great Lakes Symposium on VLSI, GLSVLSI 2017 ; Conference date: 10-05-2017 Through 12-05-2017",

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AU - Yuan, Zihao

AU - Li, Ji

AU - Li, Zhe

AU - Ding, Caiwen

AU - Ren, Ao

AU - Yuan, Bo

AU - Qiu, Qinru

AU - Draper, Jeffrey

AU - Wang, Yanzhi

PY - 2017/5/10

Y1 - 2017/5/10

N2 - Recently, Deep Convolutional Neural Networks (DCNNs) have made tremendous advances, achieving close to or even better accuracy than human-level perception in various tasks. Stochastic Computing (SC), as an alternate to the conventional binary computing paradigm, has the potential to enable massively parallel and highly scalable hardware implementations of DCNNs. In this paper, we design and optimize the SC based Softmax Regression function. Experiment results show that compared with a binary SR, the proposed SC-SR under longer bit stream can reach the same level of accuracy with the improvement of 295X, 62X, 2617X in terms of power, area and energy, respectively. Binary SR is suggested for future DCNNs with short bit stream length input whereas SC-SR is recommended for longer bit stream.

AB - Recently, Deep Convolutional Neural Networks (DCNNs) have made tremendous advances, achieving close to or even better accuracy than human-level perception in various tasks. Stochastic Computing (SC), as an alternate to the conventional binary computing paradigm, has the potential to enable massively parallel and highly scalable hardware implementations of DCNNs. In this paper, we design and optimize the SC based Softmax Regression function. Experiment results show that compared with a binary SR, the proposed SC-SR under longer bit stream can reach the same level of accuracy with the improvement of 295X, 62X, 2617X in terms of power, area and energy, respectively. Binary SR is suggested for future DCNNs with short bit stream length input whereas SC-SR is recommended for longer bit stream.

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ER -

Softmax regression design for stochastic computing based deep convolutional neural networks

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