View Proposal

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Proposer

Rob Stewart

Title

Comparing deep learning accelerator hardware

Goal

Measure the performance and usability of the TPU and Neural Compute Stick neural network accelerators

Description

"AI at the edge" allows autonomous devices and smart sensors to perform tasks such as object detection, classification, speech recognition and complex text processing tasks -- in real time and with very low power requirements. This project will compare the performance and usability of two neural network accelerator devices: The a Google TPU on a Coral.AI USB, and an Intel Neural Compute stick USB. If the student wishes to go further, a third comparator would be programming a neural network into hardware fabric with an FPGA.

Resources

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Rob Stewart

Keywords

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Computer Science for Cyber Security
Master of Science in Computing (2 Years)
Master of Science in Network Security
Master of Science in Robotics
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Bachelor of Engineering in Robotics
Bachelor of Science in Computer Science (Cyber Security)
Postgraduate Diploma in Artificial Intelligence
BSc Data Sciences
MSc Applied Cyber Security