Sumanth Gudaparthi

I am a PhD candidate in the Computer Science department at the University of Utah. I am advised by Prof. Rajeev Balasubramonian at the Utah Arch Lab. I am interested in a wide range of topics spreading Computer Arcitecture, Neural networks and VLSI architectures.

Prior to joining the U in 2017, I was working as a design Engineer for Advanced Micro Devices (AMD), Hyderabad.

Apart from work, I blog on diverse topics like explaining computer architecture to a layman, tutorials on how to work with various tools etc.

Contact Details

sgudapar [at] cs [dot] utah [dot] edu

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Education

University of Utah

PhD in Computer Science • Present


International Institute of Information Technology(IIIT)

Masters in Technology (M.Tech) in VLSI & Computer Engineering • April 2015


GITAM University

Bachelors in Technology (B.Tech) in Electrical & Electronics Engineering • April 2013


Work

Google

Research Intern • May 2020 - August 2020

Memory optimization, cache hierarchy analysis, and profiling of fleet data in Google datacenters.

Advanced Micro Devices (AMD) Research

Co-Op Engineer • May 2019 - August 2019

Exploring Memory Security, and Process In Memory (PIM) architectures.

University of Utah

Research Assistant • August 2017 - Present

Currently exploring ASIC accelerator architectures for training and inference of Neural Networks.

Advanced Micro Devices (AMD)

Design Engineer • May 2016 - July 2017

I was working on Industrial grade server and client micro architectures. Major part of my work focussed on SoC security verification of AMD chip sets.

Smarttrak Solar Systems

Design Engineer • June 2015 - April 2016

A major part of my work was focussed on designing, building and testing of the controllers for the solar tracking systems. I also worked on making boost controllers and FPGA based tracking systems for the high power solar tracking systems.

Conferences

Energy-Efficient VLSI Architecture & Implementation of Bi-Modal Multi-Banked Register-File Organization

VDAT • 2017

Workshops

Moving CNN Accelerator Computations Closer to Data

EMC2, held in conjunction with ASPLOS • 2018