About Me
I am a postdoctoral researcher in the computer science department at the University of Illinois at Urbana-Champaign, working with Dr. Vikram Adve. and Dr. Sasa Misailovic. I completed my PhD from the University of Illinois working under the supervision of Dr. Vikram Adve.
My research interests lie at the intersection of Compilers, Approximate Computing, Systems, Deep Learning, and Static Analysis. I am particularly enthusiastic about building compiler infrastructure for approximate computing with the goal of improving application performance and energy-efficiency on resource-constrained edge systems. I take interest in developing abstractions, analyses, and techniques that enable the flexible use of approximate computing techniques with minimal user involvement.
Research Areas
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Compilers
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Systems for Machine Learning
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Approximate Computing
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Static Analysis
News
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[April 2021] Released HPVM v1.0 available here
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[March 2021] Presented ApproxTuner at PPoPP'21 (Virtual) - [VIDEO]
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[January 2021] Passed Ph.D. Final Thesis Defense
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[November 2021] ApproxTuner paper accepted at PPoPP'21
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[October 2020] Presented ApproxTuner at LLVM-dev'20
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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
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[September 2017] Presented OpenMP-UVM in OpenMPCon'17
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[January 2016] Joined the LLVM group at UIUC supervised by Dr. Vikram Adve
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[August 2014] Joined the Computer Science PhD program at UIUC
Projects
ApproxTuner is an automatic framework for accuracy-aware optimization of tensor-based applications while requiring only high-level end-to-end quality specifications. The key contribution in ApproxTuner is a novel three-phase approach to approximation-tuning that consists of development-time, install-time, and run-time phases. To enable efficient autotuning, we present a novel tuning technique called predictive approximation-tuning, which significantly speeds up autotuning by analytically predicting the accuracy impacts of approximations. We evaluate 10 convolutional neural networks (CNNs) and a combined CNN and image processing benchmark, achieving a mean speedup of 2.1x (max 2.7x) on a GPU, and 1.3x mean speedup (max 1.9x) on the CPU, while staying within 1 percentage point of inference accuracy loss. ApproxTuner code is available as part of HPVM release v1.
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 benchmarks, we observe code size reductions of 20% on average.