Name
Abstract | ||
|---|---|---|
Neuromorphic vision algorithms are biologically inspired models that follow the processing that takes place in the primate visual cortex. Despite their efficiency and robustness, the complexity of these algorithms results in reduced performance when executed on general purpose processors. This paper proposes an application-specific system for accelerating a neuromorphic vision system for object recognition. The system is based on HMAX, a biologically-inspired model of the visual cortex. The neuromorphic accelerators are validated on a multi-FPGA system. Results show that the neuromorphic accelerators are 13.8脳 (2.6脳) more power efficient when compared to CPU (GPU) implementation. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s11265-012-0699-x | Signal Processing Systems |
Keywords | Field | DocType |
Domain-specific acceleration,Power efficiency,Neuromorphic systems | Electrical efficiency,Visual cortex,Machine vision,Computer science,Neuromorphic engineering,Robustness (computer science),Hardware acceleration,Computer hardware,Vision algorithms,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
70 | 2 | 1939-8018 |
Citations | PageRank | References |
3 | 0.40 | 14 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Al Maashri | 1 | 95 | 8.62 |
| Matthew Cotter | 2 | 82 | 7.18 |
| Nandhini Chandramoorthy | 3 | 81 | 9.02 |
| Michael Debole | 4 | 124 | 9.87 |
| Chi-Li Yu | 5 | 39 | 5.45 |
| Narayanan Vijaykrishnan | 6 | 6955 | 524.60 |
| Chaitali Chakrabarti | 7 | 1978 | 184.17 |