designing fpga applications through intelligent design space exploration

Department: Computer Science & Engineering
Faculty Advisor(s): Ryan Kastner

Primary Student
Name: Quentin Kevin Gautier
Phone: 619-908-0899
Grad Year: 2018

Student Collaborators
Alric Althoff,

FPGAs are becoming an important hardware to improve the ratio compute power / energy utilization for many applications. Robotics applications are notably impacted by requiring more processing power for decision making, while often running on limited power. However, designing an efficient application for FPGA is very time-consuming, and a single design can potentially take several hours to map onto the hardware. Each application is usually composed of many parameters that can affect the running time, accuracy, and other desired objectives. Due to the very large space of possible combinations of these parameters, it is often difficult - even for an expert - to predict the outcome of one particular design. We propose smart design space exploration algorithms based on active learning techniques, capable of selecting the most optimal architectures among a pool of possible designs. We also propose tunable FPGA designs to generate and analyze design spaces for multiple applications, and improve the design space exploration methods. We specifically analyze SLAM (Simultaneous Localization And Mapping) design spaces to improve the implementation of such algorithms on FPGA hardware.

Industry Application Area(s)
Software, Analytics

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