Abstract

Machine learning is growing at an exponential rate in industry today, with contemporary machine learning models having parameter counts in the high hundred millions to trillions paired with a huge computing requirement. As the field continues to grow and the models get larger and more computationally expensive, there is a greater need for specialized hardware that can accelerate the inference and training computations that are required of these machine learning models. The design of a custom hardware device specifically made to accelerate machine learning models is very intricate, with software and hardware development that needs to work together at the cutting edge. The following paper will cover these hardware accelerators and focus specifically on the Grayskull e150, a chip optimized for machine learning inference developed by Tenstorrent. Tenstorrent is a hardware company out of Toronto, Canada. This paper will detail the hardware design choices in the Grayskull that make it optimal for machine learning inference, along with the software design that facilitates compatibility with a large number of machine learning frameworks and models. The paper will end with tests of the Grayskull card on various machine learning models, comparing its performance against a Nvidia GPU and an Intel CPU.

Publication Date

5-2024

Document Type

Master's Project

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical and Microelectronic Engineering, Department of

College

Kate Gleason College of Engineering

Advisor

Mark A. Indovina

Advisor/Committee Member

Ferat Sahin

Campus

RIT – Main Campus

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