Gus Henry Smith

gussmith@cs.washington.edu – justg.us

Education

since 2018

University of Washington

Paul G. Allen Center for Computer Science & Engineering

Pursuing Ph.D., coadvised by Luis Ceze and Zach Tatlock. Focus: using tools from Programming Languages to automatically generate compilers for custom hardware.

2013–2018

Penn State

The Schreyer Honor’s College

M.S./B.S. in Computer Science and Engineering. Advised by Vijay Narayanan and John Sampson.

Publications

Specialized Accelerators and Compiler Flows: Replacing Accelerator APIs with a Formal Software/Hardware Interface. Arxiv 2022 (link). Bo-Yuan Huang, Steven Lyubomirsky, Yi Li, Mike He, Thierry Tambe, Gus Henry Smith, Akash Gaonkar, Vishal Canumalla, Gu-Yeon Wei, Aarti Gupta, Zachary Tatlock, Sharad Malik. (The 3LA paper.)

Pure Tensor Program Rewriting via Access Patterns (Representation Pearl). MAPS 2021. Gus Henry Smith, Andrew Liu, Steven Lyubomirsky, Scott Davidson, Joseph McMahan, Michael Taylor, Luis Ceze, Zachary Tatlock. (The Glenside paper.)

From DSLs to Accelerator-Rich Platform Implementations: Addressing the Mapping Gap. LATTE 2021. Bo-Yuan Huang, Steven Lyubomirsky, Thierry Tambe, Yi Li, Mike He, Gus Smith, Gu-Yeon Wei, Aarti Gupta, Sharad Malik, Zachary Tatlock.

Enumerating Hardware-Software Splits with Program Rewriting. YArch 2020. Gus Smith, Zachary Tatlock, Luis Ceze.

A FerroFET-Based In-Memory Processor for Solving Distributed and Iterative Optimizations via Least-Squares Method. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2019. Insik Yoon, Muya Chang, Kai Ni, Matthew Jerry, Samantak Gangopadhyay, Gus Henry Smith, Tomer Hamam, Justin Romberg, Vijaykrishnan Narayanan, Asif Khan, Suman Datta, Arijit Raychowdhury.

Designing Processing in Memory Architectures via Static Analysis of Real Programs. MS Thesis, 2018.

Computing With Networks of Oscillatory Dynamical Systems. Proceedings of the IEEE, 2018. Arijit Raychowdhury, Abhinav Parihar, Gus Henry Smith, Vijaykrishnan Narayanan, György Csaba, Matthew Jerry, Wolfgang Porod, Suman Datta.

A FeFET Based Processing-In-Memory Architecture for Solving Distributed Least-Square Optimizations. DRC 2018. Insik Yoon, Muya Chang, Kai Ni, Matthew Jerry, Samantak Gangopadhyay, Gus Smith, Tomer Hamam, Vijayakrishan Narayanan, Justin Romberg, Shih-Lien Lu, Suman Datta, Arijit Raychowdhury.

Third Eye: A Shopping Assistant for the Visually Impaired. IEEE Computer, 2017. Peter A Zientara, Sooyeon Lee, Gus H Smith, Rorry Brenner, Laurent Itti, Mary B Rosson, John M Carroll, Kevin M Irick, Vijaykrishnan Narayanan.

Research Projects

since Fall 2021

Lakeroad

Lead Researcher; UW SAMPL Lab/UW PLSE Lab/Real-time Machine Learning

Automatically synthesizing target-specific FPGA dialect implementations for various FPGAs from a functional description of the FPGA architecture using solver-aided programming (via Rosette). Exploring potential FPGA dialects using program rewriting via equality saturation with egg.

since Winter 2020

3LA

Contributor; UW PLSE Lab, w/ colleagues at Princeton and Harvard

Designing a methodology for verifiably mapping deep learning models to
custom accelerators. Utilizing Glenside to expose mappings in workloads.

2020–2022

Glenside

Lead Researcher; UW SAMPL Lab/Real-time Machine Learning

Designed a pure, binder-free intermediate language for optimizing low-level tensor programs via program rewriting. (See Pure Tensor Program Rewriting via Access Patterns.) Used the language to map computations to custom hardware. (See Specialized Accelerators and Compiler Flows: Replacing Accelerator APIs with a Formal Software/Hardware Interface.)

2018–2020

Bring Your Own Datatypes

Lead Researcher; UW SAMPL Lab

Enabled the exploration of new, nontraditional datatypes (i.e., alternatives to IEEE 754 floating point) with an extension to TVM, a deep learning compiler. My qualifying exam project for my Ph.D.

2017–2018

Static Analysis for Processing in Memory Accelerator Design

Master’s Project; PSU Microsystems Design Lab

Given a model of accelerating computation using processing in memory, used LLVM to detect potentially offloadable code sections within workloads.

2014–2018

ThirdEye: Shopping Assistant for the Visually Impaired

Contributor, Lead Researcher; PSU Microsystems Design Lab

Built a wearable system to assist the visually impaired in shopping. My undergraduate research.

Industry Experience

Fall 2021–Summer 2022

Google

Student Researcher (part-time)

Continued my previous work developing a learned cost model for configuring sparse tensor kernels.

Summer 2021

Google

Software Engineering Intern, MLIR

Developed a learned cost model for configuring sparse tensor kernels. In addition, contributed to the MLIR sparse tensor dialect.

Summer 2019

Microsoft

Research Intern, AI and Advanced Architectures

Statically analyzed deep learning workloads to inform architecture design.

Summer 2018

Google

Software Engineering Intern, Fuchsia

Implemented the RFCOMM protocol for one of Google’s OSes, Fuchsia.

Summer 2017

Google

Software Engineering Intern, Chrome

Helped the Chrome Remote Desktop team identify and implement optimizations for embedded devices such as the Raspberry Pi.

Summer 2016

Google

Software Engineering Intern, Android Internal Tools

Contributed to Java-based Android profiling tools.

Service

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