Investigation on mlp artificial neural network using FPGA for autonomous cart follower system
| dc.contributor.author | Liew Yeong Tat | |
| dc.date.accessioned | 2021-04-14T03:13:40Z | |
| dc.date.available | 2021-04-14T03:13:40Z | |
| dc.date.issued | 2015-07-01 | |
| dc.description.abstract | The future of the autonomous cart follower system will equipped with lots of sensory data, due to the ever lower cost of sensory device. This provides design challenge on handling large data and firmware complexity. Most of the existing systems are implemented via usage of microcontroller board, which has limited performance and expansion is not possible without replacement of newer board. The project proposes an alternative approach of running the autonomous cart follower systems on neural network model using Field Programmable Gates Array (FPGA). A microcontroller based autonomous cart follower systems is modified to use the FPGA board and implemented via the System on Chip (SOC) approach. The neural network is trained offline in simulation tools with training vector collected from running the existing autonomous cart follower systems. The trained neural network model then implemented as software code in the SOC. By observation the firmware footprint of the neural network model remains small size regardless of the neural network size. The result shows that with 40% more additional resource utilization, the overall system improvement of 27 times is achieved with the usage of hardware acceleration block in SOC compared to SOC without hardware acceleration. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/12806 | |
| dc.language.iso | en | en_US |
| dc.title | Investigation on mlp artificial neural network using FPGA for autonomous cart follower system | en_US |
| dc.type | Thesis | en_US |
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