Abelardo L.
About Abelardo L.
Abelardo L. serves as the Director of Compilers and Embedded Systems at Latent AI, Inc. since 2021, focusing on optimizing AI implementations and adaptive edge AI processing.
Current Role at Latent AI, Inc.
Abelardo L. serves as the Director of Compilers and Embedded Systems at Latent AI, Inc. since 2021. In this role, he focuses on solutions that facilitate adaptive edge AI processing. He contributes to the development of the Latent AI Efficient Inference Platform (LEIP™), which aims to optimize artificial intelligence for compute, energy, and memory efficiency. His work integrates both hardware and software components to achieve optimal implementations tailored to specific constraints.
Educational Background
Abelardo L. has an extensive educational background in electrical engineering and computer engineering. He earned a Bachelor of Science in Electrical Engineering (BSEE) from Tecnológico de Monterrey from 1984 to 1988. He further pursued a Master of Science in Electrical Engineering (MSEE) at Georgia Institute of Technology from 1990 to 1991. He completed his Ph.D. in Electrical and Computer Engineering at Georgia Institute of Technology from 1993 to 1997. Additionally, he undertook postdoctoral studies in electrical engineering at Stanford University from 1998 to 1999.
Previous Work Experience
Abelardo L. has held various academic and industry positions throughout his career. He worked at Stanford University as a Research Assistant (Postdoctorate) from 1997 to 1998. He was an Associate Professor at TECNOLÓGICO DE MONTERREY from 2012 to 2016 and served as a Full-Time Professor from 1998 to 2001 and again from 2016 to 2019. He also worked as an Assistant Professor at TECNOLÓGICO DE MONTERREY from 2005 to 2012. In the industry, he was a Staff Electrical Engineer at Motorola from 2002 to 2005 and held the position of Executive Scientist at Latent AI, Inc. from 2019 to 2021.
Research Interests and Contributions
Abelardo L. has research interests that encompass high-performance architectures, machine learning, and compilers. His work emphasizes the exploration of both hardware and software to achieve efficient implementations. He actively contributes to advancements in AI technologies, particularly through his role in developing the Latent AI Efficient Inference Platform (LEIP™), which focuses on enhancing the efficiency of AI applications in terms of computation, energy consumption, and memory usage.