Title: FPGA Architecture for Deep Learning and its application to Planetary Robotics,
Abstract: Autonomous control systems onboard planetary rovers and spacecraft benefit
from having cognitive capabilities like learning so that they can adapt to
unexpected situations in-situ. Q-learning is a form of reinforcement learning
and it has been efficient in solving certain class of learning problems.
However, embedded systems onboard planetary rovers and spacecraft rarely
implement learning algorithms due to the constraints faced in the field, like
processing power, chip size, convergence rate and costs due to the need for
radiation hardening. These challenges present a compelling need for a portable,
low-power, area efficient hardware accelerator to make learning algorithms
practical onboard space hardware. This paper presents a FPGA implementation of
Q-learning with Artificial Neural Networks (ANN). This method matches the
massive parallelism inherent in neural network software with the fine-grain
parallelism of an FPGA hardware thereby dramatically reducing processing time.
Mars Science Laboratory currently uses Xilinx-Space-grade Virtex FPGA devices
for image processing, pyrotechnic operation control and obstacle avoidance. We
simulate and program our architecture on a Xilinx Virtex 7 FPGA. The
architectural implementation for a single neuron Q-learning and a more complex
Multilayer Perception (MLP) Q-learning accelerator has been demonstrated. The
results show up to a 43-fold speed up by Virtex 7 FPGAs compared to a
conventional Intel i5 2.3 GHz CPU. Finally, we simulate the proposed
architecture using the Symphony simulator and compiler from Xilinx, and
evaluate the performance and power consumption.