FPGA-accelerator system for computing biologically inspired feature extraction models

Michael DeBole; Yang Xiao; Chi Li Yu; Ahmed Al Maashri; Matthew Cotter; Chaitali Chakrabarti; Vijaykrishnan Narayanan

doi:10.1109/ACSSC.2011.6190106

FPGA-accelerator system for computing biologically inspired feature extraction models

Michael DeBole^*, Yang Xiao, Chi Li Yu, Ahmed Al Maashri, Matthew Cotter, Chaitali Chakrabarti, Vijaykrishnan Narayanan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Neuromorphic algorithms for computer-based vision may be the next step towards improving the way computers gather and interpret visual information. However, these algorithms typically have high computational demands making them difficult to deploy in embedded environments where power consumption is equally as important as performance. In this paper, we present an embedded implementation of a ventral visual pathway model, HMAX. We describe an embedded FPGA system that implements the model, as well as accelerator engines necessary to ensure adequate performance. The final system is shown to operate within a power budget of 3W while achieving up to 16.5X speedup over a pure embedded processor implementation.

Original language	English
Title of host publication	Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages	751-755
Number of pages	5
DOIs	https://doi.org/10.1109/ACSSC.2011.6190106
Publication status	Published - 2011
Externally published	Yes
Event	45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States Duration: Nov 6 2011 → Nov 9 2011

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Country/Territory	United States
City	Pacific Grove, CA
Period	11/6/11 → 11/9/11

Keywords

Embedded Hardware
FPGA
Neuromorphic vision algorithms
Signal Processing Hardware

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2011.6190106

Cite this

DeBole, M., Xiao, Y., Yu, C. L., Al Maashri, A., Cotter, M., Chakrabarti, C., & Narayanan, V. (2011). FPGA-accelerator system for computing biologically inspired feature extraction models. In Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 (pp. 751-755). [6190106] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2011.6190106

FPGA-accelerator system for computing biologically inspired feature extraction models. / DeBole, Michael; Xiao, Yang; Yu, Chi Li et al.

Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011. 2011. p. 751-755 6190106 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

DeBole, M, Xiao, Y, Yu, CL, Al Maashri, A, Cotter, M, Chakrabarti, C & Narayanan, V 2011, FPGA-accelerator system for computing biologically inspired feature extraction models. in Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011., 6190106, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 751-755, 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011, Pacific Grove, CA, United States, 11/6/11. https://doi.org/10.1109/ACSSC.2011.6190106

DeBole M, Xiao Y, Yu CL, Al Maashri A, Cotter M, Chakrabarti C et al. FPGA-accelerator system for computing biologically inspired feature extraction models. In Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011. 2011. p. 751-755. 6190106. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2011.6190106

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abstract = "Neuromorphic algorithms for computer-based vision may be the next step towards improving the way computers gather and interpret visual information. However, these algorithms typically have high computational demands making them difficult to deploy in embedded environments where power consumption is equally as important as performance. In this paper, we present an embedded implementation of a ventral visual pathway model, HMAX. We describe an embedded FPGA system that implements the model, as well as accelerator engines necessary to ensure adequate performance. The final system is shown to operate within a power budget of 3W while achieving up to 16.5X speedup over a pure embedded processor implementation.",

keywords = "Embedded Hardware, FPGA, Neuromorphic vision algorithms, Signal Processing Hardware",

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AU - Chakrabarti, Chaitali

AU - Narayanan, Vijaykrishnan

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AB - Neuromorphic algorithms for computer-based vision may be the next step towards improving the way computers gather and interpret visual information. However, these algorithms typically have high computational demands making them difficult to deploy in embedded environments where power consumption is equally as important as performance. In this paper, we present an embedded implementation of a ventral visual pathway model, HMAX. We describe an embedded FPGA system that implements the model, as well as accelerator engines necessary to ensure adequate performance. The final system is shown to operate within a power budget of 3W while achieving up to 16.5X speedup over a pure embedded processor implementation.

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FPGA-accelerator system for computing biologically inspired feature extraction models

Abstract

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Keywords

ASJC Scopus subject areas

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