Name
Abstract | ||
|---|---|---|
Algorithms based on Convolutional Neural Network (CNN) have recently been applied to object detection applications, greatly improving their performance. However, many devices intended for these algorithms have limited computation resources and strict power consumption constraints, and are not suitable for algorithms designed for GPU workstations. This paper presents a novel method to optimise CNN-based object detection algorithms targeting embedded FPGA platforms. Given parameterised CNN hardware modules, an optimisation flow takes network architectures and resource constraints as input, and tunes hardware parameters with algorithm-specific information to explore the design space and achieve high performance. The evaluation shows that our design model accuracy is above 85% and, with optimised configuration, our design can achieve 49.6 times speed-up compared with software implementation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-56258-2_22 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Design space,Object detection,Convolutional neural network,Computer science,Network architecture,Algorithm,Field-programmable gate array,Workstation,Computation,Power consumption | Conference | 10216 |
ISSN | Citations | PageRank |
0302-9743 | 11 | 0.60 |
References | Authors | |
10 | 5 | |