ECpE Seminar Series with Biresh Kumar Joardar: Computing Systems and Machine Learning: The New Alliance

Date/Time
Date(s) - 7 Feb 2022
9:50 AM - 11:30 AM

Location
3043 ECpE Building Addition

Headshot photo of Biresh Kumar JoardarSpeaker: Biresh Kumar Joardar, Computing Innovation (Postdoctoral) Fellow at the Department of Electrical and Computer Engineering at Duke University

Title: Computing Systems and Machine Learning: The New Alliance

Abstract: Advanced computing systems have long been enablers for breakthroughs in machine learning (ML). However, as ML applications become more complex, and size of the datasets increase, existing computing platforms are no longer sufficient to meet the growing demands of the ML applications. The slowing down of Moore’s law has impacted the development of new computing platforms, which is detrimental to future developments in ML. Advancement in novel ML algorithms and computer system design are tightly coupled and advancement in one cannot be achieved without the other. New computing systems developed using emerging technologies (e.g., 3D integration and processing-in-memory (PIM)) and ML algorithms can together bridge this gap. For instance, Monolithic 3D (M3D) integration is a breakthrough technology to achieve “More Moore and More Than Moore” and provides numerous benefits by utilizing vertical interconnects and 3D stacking but comes with various challenges such as large design space and heterogeneous integration. ML-based design space exploration algorithms can significantly reduce the design effort and time to find good quality M3D designs that achieve better power, performance and area than traditional designs.

In this presentation, we will discuss how machine learning techniques can be used to solve complex hardware design problems (and vice versa). More specifically, we will highlight the alliance between hardware design and machine learning. We will demonstrate how machine learning techniques can be used for advancing hardware designs spanning edge devices to cloud, which will empower further advances in machine learning (i.e., Machine learning for machine learning).

Bio: Biresh Kumar Joardar is currently an NSF-sponsored Computing Innovation (postdoctoral) Fellow at the Department of Electrical and Computer Engineering at Duke University. He obtained his PhD from Washington State University in 2020. His PhD research focused on using machine learning algorithms to design and optimize heterogeneous manycore systems. As a CI Fellow, Biresh is currently working on developing reliable and energy-efficient architectures for machine learning applications. He received the ‘Outstanding Graduate Student Researcher Award’ at Washington state University in 2019. Biresh has published in numerous prestigious conferences (including ESWEEK, DATE, ICCAD) and journals (TC, TCAD and TECS). His work have been nominated for Best Paper Awards at DATE 2019 and DATE 2020. He won the best paper award in NOCS 2019. His current research interests include machine learning, manycore architectures, accelerators for deep learning, hardware reliability and security.

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