I'd describe myself as an avid technologist who thrives at the intersection of computer architecture, deep learning algorithms, hardware design and cloud computing. Over the past decade, my primary research and development efforts have centered around the invention and development of specialized platforms that have dramatically improved industry-wide AI systems’ performance and revolutionized the incorporation of AI capabilities within IBM.
My team's contributions to hardware-aware deep learning algorithms have led to cutting-edge work that has been instrumental in launching IBM’s AI Hardware effort and the development of multiple generations of AI-optimized hardware engines. We've developed foundational low-precision algorithms and hardware designs for both deep learning training and inference leading to transformative impact on industry-wide design customizations for AI and machine learning. This research has also led to the establishment of a precision scaling roadmap for AI hardware performance enhancements - akin to how Moore's law has guided silicon scaling for the past 5 decades.
Having launched the IBM Research AI Hardware Center in collaboration with the state of NY and other industry partners, my efforts continue to focus on integrating specialized AI capabilities across a range of Enterprise and Hybrid-Cloud systems, and to broaden the ecosystem of the AI Hardware Center.
Background: I've a Bachelors in Electrical Engineering from the Indian Institute of Technology, Bombay and a Ph.D. from Stanford University. I'm currently an IBM Fellow, IBM Member Academy of Technology and Senior Manager at the IBM Research Center in Yorktown Heights, NY.