Design Space Exploration Scheme for Mapping Convolutional Neural Network on Zynq Zedboard
| Autor: | M. Sohaib Ul Hassan, Sajid Gul Khawaja, Umar Shahbaz Khan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Scheme (programming language) Speedup Computer science business.industry Design space exploration General purpose computer Robotics 02 engineering and technology 01 natural sciences Convolutional neural network 020202 computer hardware & architecture Computer architecture 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Artificial intelligence Field-programmable gate array business computer Implementation computer.programming_language |
| Zdroj: | 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). |
| DOI: | 10.1109/ecai50035.2020.9223137 |
| Popis: | Convolutional Neural Network (CNN) is an important machine learning algorithm. Due to its broad applications and classification accuracy it has become hot topic in recent times. CNNs are both computationally expensive and have extensive memory accesses which has rendered it inefficient on general purpose computers. GPU implementations have improved the performance of algorithm but high energy consumption of GPUs doesn't allow its usage in robotics and mobile embedded platforms. In this paper we study the mapping of Convolutional Neural Networks on field programmable gate arrays (FPGAs). We implement a VGG-16 style network which are the most admired CNN architectures in community. We Used Xilinx Zynq Zedboard for analytical modeling and mapping of CNN. For a complete network implementation, we achieved a peak performance of 1.3 GMACCs at 120 MHz frequency. Our implementation achieves a speed up of 4 times compared to software implementation on General Purpose Computer. |
| Databáze: | OpenAIRE |
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