Investigation on mlp artificial neural network using FPGA for autonomous cart follower system
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Date
2015-07-01
Authors
Liew Yeong Tat
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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.