Enabling Energy-efficient AI Computing: Leveraging Application-specific Approximations by Akash Kumar

Speaker: Akash Kumar (Ruhr University Bochum)
Details: Tue, 12 Aug, 3:30 PM, SIT 001

Abstract: The widespread adoption of Artificial intelligence and Machine Learning (AI/ML) models across various fields, such as healthcare, autonomous vehicles, smart agriculture, and industrial automation, has led to a growing demand for efficient and scalable AI/ML solutions. However, as AI/ML algorithms grow more complex, their substantial memory requirements and high energy consumption pose significant challenges for deployment on resource-constrained embedded systems, such as wearable health monitors and IoT devices.

In this talk, I will first introduce the topic and outline the significance of cross-layer approximation framework, emphasizing the necessity of a generic and scalable approach to designing approximate arithmetic operators. I will then talk about platform-specific optimizations for designing approximate operators optimized for FPGAs and end with how modern AI/ML-based DSE approaches can be used for approximate computer arithmetic.

Biography: Akash Kumar received the joint Ph.D. degree in electrical engineering and embedded systems from the Eindhoven University of Technology, Eindhoven, The Netherlands, and the National University of Singapore (NUS), Singapore, in 2009. From 2009 to 2015, he was with NUS. From October 2015 until March 2024, he was a Professor with Technische Universität Dresden, Dresden, Germany, where he was directing the Chair for Processor Design. Since April 2024, he is directing the chair of Embedded Systems at Ruhr University Bochum, Germany. His research interests include the design and analysis of low-power embedded multiprocessor systems and designing secure systems with emerging nano-technologies.