Tyler Kenney
About Tyler Kenney
Tyler Kenney is a Systems & Compiler Engineer specializing in machine learning acceleration, currently working at Lightmatter since 2018. He has a background in hardware/software co-design and has previously held positions at Netezza and IBM Netezza.
Current Role at Lightmatter
Tyler Kenney currently serves as a Systems & Compiler Engineer specializing in Machine Learning Acceleration at Lightmatter. He has held this position since 2018, contributing to the development of technologies aimed at enhancing machine learning performance. His role involves hardware/software co-design, focusing on the integration of systems to optimize performance in machine learning applications.
Previous Experience at Netezza and IBM
Before joining Lightmatter, Tyler Kenney worked at IBM Netezza as a Systems & Compiler Engineer with a focus on FPGA Acceleration from 2014 to 2018. Prior to this, he completed a three-month internship at Netezza in 2013, where he gained experience in FPGA acceleration. His background includes significant experience in compiler technologies and systems engineering.
Internship Experience at EMC
Tyler Kenney has also gained valuable experience through internships at EMC. He worked as a Software Engineer Intern in the Unified Storage Division for three months in 2012 and as a SQA Engineer Intern for two months in 2011. These roles provided him with foundational skills in software development and quality assurance.
Education and Academic Background
Tyler Kenney earned a Master of Science in Computer Engineering from Lehigh University, completing his studies from 2013 to 2014. He also holds a Bachelor of Science in Computer Engineering from the same institution, where he studied from 2009 to 2013. His academic background supports his expertise in hardware/software co-design and compiler technologies.
Technical Skills and Specializations
Tyler Kenney specializes in hardware/software co-design, focusing on optimizing the integration of hardware and software systems. He is experienced in using LLVM, a collection of modular and reusable compiler and toolchain technologies, which is essential for machine learning acceleration. His technical skills contribute to advancements in performance optimization in various applications.