Ken Shiring
About Ken Shiring
Ken Shiring is a Senior Principal Engineer at SiMa.ai, specializing in novel computer architectures and custom software tools. He has extensive experience in hardware and software engineering, having worked at various companies including IBM, Cadence Design Systems, and MediaTek.
Work at SiMa.ai
Ken Shiring has been employed at SiMa.ai as a Senior Principal Engineer since 2022. In this role, he focuses on guiding the development of future chip architectures by leveraging innovative machine learning models and algorithmic solutions. His work emphasizes the co-design of software and hardware to address complex computing challenges. SiMa.ai specializes in providing machine learning solutions for edge devices, and Shiring's expertise contributes to advancing the company's technology in this area.
Previous Experience
Prior to his current position, Ken Shiring held various engineering roles across multiple companies. He worked at Annapolis Micro Systems as an Assistant Engineer for seven months in 1997. He then served as a Design/Verification Engineer at IBM from 1999 to 2004. Following that, he was a Senior Member of Consulting Staff at Cadence Design Systems from 2007 to 2012. Shiring also worked as a Masters Researcher at the University of Southern California from 2005 to 2007, and as a Director of Core Machine Learning Technologies at Wave Computing from 2017 to 2019.
Education and Expertise
Ken Shiring earned his Master of Science degree in Computer Engineering from the University of Southern California, where he studied from 2005 to 2007. He also holds a Bachelor of Science degree in Computer Engineering from Virginia Tech, obtained from 1994 to 1998. His educational background supports his expertise in digital logic design, verification, Electronic Design Automation, and machine learning. Shiring's knowledge spans both datacenter-scale and mobile-scale hardware and software engineering.
Specializations
Ken Shiring specializes in novel computer architectures and custom software tools designed to maximize modern hardware capabilities. His work involves co-designing software and hardware to solve complex computing challenges. He has provided feedback on the CRISP-EML framework, which focuses on embedded machine learning development. Shiring's experience in various engineering roles has equipped him with a comprehensive understanding of machine learning technologies and their applications in hardware design.