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Computer Science

D-Index
82
Citations
20725
World Ranking
984
National Ranking
528

Research.com Recognitions

  • 2020 - ACM Fellow For contributions in compiler code generation for instruction level parallelism, and customized microprocessor architectures
  • 2013 - ACM Senior Member

Overview

Scott Mahlke is a researcher affiliated with the University of Michigan-Ann Arbor in the United States. Their main area of expertise lies within computer science, with a significant focus on artificial intelligence, computer networks and communications, computer vision and pattern recognition, software, and automotive engineering.

Their scholarly work spans multiple topics including advanced neural network applications, adversarial robustness in machine learning, software testing and debugging techniques, caching and content delivery, machine learning and data classification, autonomous vehicle technology and safety, and software reliability and analysis research.

Recent papers authored by or involving Scott Mahlke include:

  • A Systematic Framework to Identify Violations of Scenario-dependent Driving Rules in Autonomous Vehicle Software, 2021, Proceedings of the ACM on Measurement and Analysis of Computing Systems
  • BitSET: Bit-Serial Early Termination for Computation Reduction in Convolutional Neural Networks, 2023, ACM Transactions on Embedded Computing Systems
  • AVMaestro: A Centralized Policy Enforcement Framework for Safe Autonomous-driving Environments, 2022, 2022 IEEE Intelligent Vehicles Symposium (IV)
  • A Systematic Framework to Identify Violations of Scenario-dependent Driving Rules in Autonomous Vehicle Software, 2021, ACM SIGMETRICS Performance Evaluation Review
  • LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme, 2024, arXiv (Cornell University)

Scott Mahlke has collaborated frequently with several co-authors, including Reetuparna Das, Qingzhao Zhang, David Hong, Qi Alfred Chen, and Z. Morley Mao.

Their publications are found in venues such as arXiv (Cornell University), ACM Transactions on Embedded Computing Systems, Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2022 IEEE Intelligent Vehicles Symposium (IV), and ACM SIGMETRICS Performance Evaluation Review.

Throughout their career, Scott Mahlke has received recognitions including becoming an ACM Senior Member in 2013 and being named an ACM Fellow in 2020 for contributions in compiler code generation for instruction level parallelism and customized microprocessor architectures.

Best Publications

  • Effective compiler support for predicated execution using the hyperblock

    Scott A. Mahlke;David C. Lin;William Y. Chen;Richard E. Hank

  • The superblock: an effective technique for VLIW and superscalar compilation

    Wen-Mei W. Hwu;Scott A. Mahlke;William Y. Chen;Pohua P. Chang

  • COMET: code offload by migrating execution transparently

    Mark S. Gordon;D. Anoushe Jamshidi;Scott Mahlke;Z. Morley Mao

  • IMPACT: an architectural framework for multiple-instruction-issue processors

    Pohua P. Chang;Scott A. Mahlke;William Y. Chen;Nancy J. Warter

  • Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism

    Jiecao Yu;Andrew Lukefahr;David Palframan;Ganesh Dasika

  • SAGE: self-tuning approximation for graphics engines

    Mehrzad Samadi;Janghaeng Lee;D. Anoushe Jamshidi;Amir Hormati

  • Using profile information to assist classic code optimizations

    Pohua P. Chang;Scott A. Mahlke;Wen-mei W. Hwu

  • SODA: A Low-power Architecture For Software Radio

    Yuan Lin;Hyunseok Lee;Mark Woh;Yoav Harel

  • Shoestring: probabilistic soft error reliability on the cheap

    Shuguang Feng;Shantanu Gupta;Amin Ansari;Scott Mahlke

  • Processor acceleration through automated instruction set customization

    Nathan Clark;Hongtao Zhong;Scott Mahlke

  • Orchestrating the execution of stream programs on multicore platforms

    Manjunath Kudlur;Scott Mahlke

  • Paraprox: pattern-based approximation for data parallel applications

    Mehrzad Samadi;Davoud Anoushe Jamshidi;Janghaeng Lee;Scott Mahlke

  • A comparison of full and partial predicated execution support for ILP processors

    Scott A. Mahlke;Richard E. Hank;James E. McCormick;David I. August

  • BulletProof: a defect-tolerant CMP switch architecture

    K. Constantinides;S. Plaza;J. Blome;B. Zhang

  • Edge-centric modulo scheduling for coarse-grained reconfigurable architectures

    Hyunchul Park;Kevin Fan;Scott A. Mahlke;Taewook Oh

  • IMPACT: an architectural framework for multiple-instruction-issue processors

    Unknown

  • Application-Specific Processing on a General-Purpose Core via Transparent Instruction Set Customization

    Nathan Clark;Manjunath Kudlur;Hyunchul Park;Scott Mahlke

  • Profile-guided automatic inline expansion for C programs

    Pohua P. Chang;Scott A. Mahlke;William Y. Chen;Wen-mei W. Hwu

  • Reliable Systems on Unreliable Fabrics

    T. Austin;V. Bertacco;S. Mahlke;Yu Cao

  • Composite Cores: Pushing Heterogeneity Into a Core

    Andrew Lukefahr;Shruti Padmanabha;Reetuparna Das;Faissal M. Sleiman

  • Effective compiler support for predicated execution using the hyperblock

    Scott A. Mahlke;David C. Lin;William Y. Chen;Richard E. Hank

  • The superblock: an effective technique for VLIW and superscalar compilation

    Wen-Mei W. Hwu;Scott A. Mahlke;William Y. Chen;Pohua P. Chang

Frequent Co-Authors

Wen-mei W. Hwu
Wen-mei W. Hwu University of Illinois at Urbana-Champaign
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Chaitali Chakrabarti
Chaitali Chakrabarti Arizona State University
Krisztian Flautner
Krisztian Flautner University of Michigan–Ann Arbor
Reetuparna Das
Reetuparna Das University of Michigan–Ann Arbor
Stéphane Lafortune
Stéphane Lafortune University of Michigan–Ann Arbor
David I. August
David I. August Princeton University
Ronald G. Dreslinski
Ronald G. Dreslinski University of Michigan–Ann Arbor
Todd Austin
Todd Austin University of Michigan–Ann Arbor
Jason Mars
Jason Mars University of Michigan–Ann Arbor

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