Kailash Gopalakrishnan

Papers

Total Citations

101

H-Index

About

Kailash Gopalakrishnan is a leading researcher in approximate computing, energy-efficient architectures, and emerging computing paradigms. His seminal work, "Approximate computing: Challenges and opportunities" (2016), with over 100 citations, systematically explores how approximation techniques can be applied to data analytics and cognitive applications, demonstrating that controlled inaccuracies can dramatically improve performance and energy efficiency without sacrificing meaningful output quality. This paper has become a foundational reference for researchers seeking to harness approximation for scalable, low-power systems. Gopalakrishnan’s broader contributions include pioneering work on near-threshold voltage computing, neuromorphic architectures, and heterogeneous integration, all aimed at pushing the boundaries of energy-constrained computing. His research has influenced both academic inquiry and industrial design practices, particularly in domains like machine learning and big data analytics. Recognized for his innovative approach, Gopalakrishnan continues to shape the future of computing by bridging the gap between theoretical promise and practical, deployable systems.