Sanmukh Kuppannagari

Sanmukh R. Kuppannagari

Ming Hsieh Department of Electrical and Computer Engineering
University of Southern California
3740 McClintock Avenue, EEB 226, Los Angeles, CA 90089
Telephone: +1 (213) 280-6229
Email: kuppanna@usc.edu
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News

May 2021 Our proposal "SaTC: CORE: Small: Accelerating Privacy Preserving Deep Learning for Real-time Secure Applications" has been funded by the U.S. National Science Foundation. PI: Viktor Prasanna. Co-PI: Sanmukh Kuppannagari.
Dec 2020 Our paper "DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference" has been accepted for publication in the proceedings of the 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, (FPGA) 2021.
Sept 2020 Our paper "How to Efficiently Train Your AI Agent? Characterizing and Evaluating Deep Reinforcement Learning on Heterogeneous Platforms" received Outstanding Student Paper Award in the 2020 IEEE High Performance Extreme Computing Virtual Conference (HPEC)).
Aug 2020 Our proposal "CNS Core: Small: AccelRITE: Accelerating ReInforcemenT Learning based AI at the Edge Using FPGAs" has been funded by the U.S. National Science Foundation. PI: Viktor Prasanna. Co-PI: Sanmukh Kuppannagari.
Dec 2019 I organized the First Workshop on Data Science for Future Energy Systems (DSFES), in conjunction with the 26th IEEE International Conference on High Performance Computing, Data, and Analytics. Please visit here for a summary of the workshop.
May 2019 Our proposal "OAC Core: Small: Scalable Graph Analytics on Emerging Cloud Infrastructure" has been funded by the U.S. National Science Foundation. PI: Viktor Prasanna. Co-PI: Sanmukh Kuppannagari.

Biography

I am a Senior Research Associate in the Department of Electrical and Computer Engineering, University of Southern California under the advisement of Dr Viktor Prasanna. I work on a diverse set of research areas which include accelerating AI algorithms on heterogeneous platforms, developing accelerators for Homomorphic Encryption based Deep Neural Networks, and developing scheduling algorithms for smart grids. I received my PhD in Computer Engineering from the University of Southern California in August 2018.

Research Interests

Accelerating Privacy Preserving Machine Learning, Accelerating Deep Reinforcement Learning, Reconfigurable Computing, Parallel Computing, Combinatorial Optimization, Approximation Algorithms

Dissertation

The focus of my PhD dissertation was to develop polynomial runtime complexity approximation algorithms to perform cost optimal supply demand matching in smart grids targeting various objective functions and constraints such as cost minimization, fairness, network constraints etc. Using both theoretical analysis and practical evaluations, we showed that our supply demand matching algorithms provide solutions which are close to optimal in a small amount of time.

PhD Defense Slides

PhD Dissertation

PhD Defense Committee: Viktor K. Prasanna (chair), Mohammed Beshir, Shaddin Dughmi, Mihailo Jovanovic, Rajgopal Kannan