About Me:
I am a 6th year Ph.D. student working in the LLVM group supervised by Dr. Vikram Adve. My research interests lie at the intersection of Compilers, Approximate Computing, Deep Learning, Systems, and Static Analysis. I am particularly enthusiastic about building compiler infrastructure that improves performance and reduces the energy usage on edge systems. I take interest in developing abstractions, analyses, and techniques that enable the use of approximations with minimal programmer/user involvement.
Research Areas:
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Compilers
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Machine Learning
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Approximate Computing
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Static Analysis
News:
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[March 2020] Passed Ph.D. Preliminary Exam
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[January 2020] Released HPVM, a retargetable compiler infrastructure for heterogeneous systems: https://gitlab.engr.illinois.edu/llvm/hpvm-release
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[October 2019] ApproxHPVM paper presented at OOPSLA'19
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[September 2019] ApproxHPVM paper accepted at OOPSLA'19
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[September 2018] TRIMMER presented at ASE'18
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[July 2018] TRIMMER accepted at ASE'18
Projects:
ApproxHPVM is a portable compiler IR and framework for accuracy-aware optimizations. ApproxHPVM includes analyses that automatically trade-off acceptable levels of accuracy for significant gains in performance and energy reduction. On popular deep learning workloads, ApproxHPVM shows performance improvements of up to 9x and energy reductions of up to 11x exploiting reduced precision compute on GPUs, and special purpose analog compute accelerators for deep learning.
TRIMMER is a software debloating infrastructure that removes application features that are unused with respect to a given user-specification. TRIMMER includes sophisticated analysis for specializing programs with respect to application-specific configuration files, inteprocedural constant propagation, and aggressive loop unrolling. For real-world world benchmarks, we observe code size reductions of 20% on average.