TY - GEN
T1 - A hardware architecture for accelerating neuromorphic vision algorithms
AU - Maashri, A. Al
AU - DeBole, M.
AU - Yu, C. L.
AU - Narayanan, V.
AU - Chakrabarti, Chaitali
PY - 2011/12/26
Y1 - 2011/12/26
N2 - Neuromorphic vision algorithms are biologically inspired algorithms that follow the processing that takes place in the visual cortex. These algorithms have proved to match classical computer vision algorithms in classification performance and even outperformed them in some instances. However, neuromorphic algorithms suffer from high complexity leading to poor execution times when running on general purpose processors, making them less attractive for real-time applications. FPGAs, on the other hand, have become true signal processing platforms due to their lightweight, low power consumption and massive parallel computational resources. This paper describes an FPGA-based hardware architecture that accelerates an object classification cortical model, HMAX. Compared to a CPU implementation, this hardware accelerator offers 23X (89X) speedup when mapped to a single-FPGA (multi-FPGA) platform, while maintaining a classification accuracy of 92.5%.
AB - Neuromorphic vision algorithms are biologically inspired algorithms that follow the processing that takes place in the visual cortex. These algorithms have proved to match classical computer vision algorithms in classification performance and even outperformed them in some instances. However, neuromorphic algorithms suffer from high complexity leading to poor execution times when running on general purpose processors, making them less attractive for real-time applications. FPGAs, on the other hand, have become true signal processing platforms due to their lightweight, low power consumption and massive parallel computational resources. This paper describes an FPGA-based hardware architecture that accelerates an object classification cortical model, HMAX. Compared to a CPU implementation, this hardware accelerator offers 23X (89X) speedup when mapped to a single-FPGA (multi-FPGA) platform, while maintaining a classification accuracy of 92.5%.
KW - FPGA
KW - Hardware
KW - Neuromorphic Hardware Architecture
KW - Neuromorphic vision algorithms
KW - Signal Processing
UR - http://www.scopus.com/inward/record.url?scp=84055222679&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84055222679&partnerID=8YFLogxK
U2 - 10.1109/SiPS.2011.6089002
DO - 10.1109/SiPS.2011.6089002
M3 - Conference contribution
AN - SCOPUS:84055222679
SN - 9781457719219
T3 - 2011 IEEE Workshop on Signal Processing Systems, SiPS 2011, Proceedings
SP - 355
EP - 360
BT - 2011 IEEE Workshop on Signal Processing Systems, SiPS 2011, Proceedings
T2 - 2011 IEEE Workshop on Signal Processing Systems, SiPS 2011
Y2 - 4 October 2011 through 7 October 2011
ER -