[HTCondor-users] [CFP: FastPath'19] International Workshop on Performance Analysis of Machine Learning Systems 2019 (Deadline: Feb 8)

[Lu Li <lu.li@xxxxxxxx>]

          ÂFastPath 2019 - CALL FOR PAPERS

 ÂInternational Workshop on Performance Analysis of Machine Learning Systems

     ÂMarch 24, 2019 - Madison, Wisconsin, United States

           ÂÂhttps://tinyurl.com/2019-FastPath

            In conjunction with ISPASS 2019:ÂÂ

           ÂÂhttp://www.ispass.org/ispass2019

KEY DATES

Submission: ÂFebruary 8, 2019

Notification:Â February 22, 2019

Workshop:  ÂMarch  24, 2019

SUBMISSION

Prospective authors must submit a 2-4 page extended abstract electronically at:

https://easychair.org/conferences/?conf=fastpath2019

SUMMARY

FastPath 2019 brings together researchers and practitioners involved in cross-stack hardware/software performance analysis, modeling, and evaluation for efficient machine learning systems. Machine learning demands tremendous amount of computing. Current machine learning systems are diverse, including cellphones, high performance computing systems, database systems, self-driving cars, robotics, and in-home appliances. Many machine-learning systems have customized hardware and/or software. The types and components of such systems vary, but a partial list includes traditional CPUs assisted with accelerators (ASICs, FPGAs, GPUs), memory accelerators, I/O accelerators, hybrid systems, converged infrastructure, and IT appliances. Designing efficient machine learning systems poses several challenges.

These include distributed training on big data, hyper-parameter tuning for models, emerging accelerators, fast I/O for random inputs, approximate computing for training and inference, programming models for a diverse machine-learning workloads, high-bandwidth interconnect, efficient mapping of processing logic on hardware, and cross system stack performance optimization. Emerging infrastructure supporting big data analytics, cognitive computing, large-scale machine learning, mobile computing, and internet-of-things, exemplify system designs optimized for machine learning at large.

TOPICS

FastPath seeks to facilitate the exchange of ideas on performance optimization of machine learning/AI systems and seeks papers on a wide range of topics including, but not limited to:

Âo Workload characterization, performance modeling and profiling of machine learning applications

Âo GPUs, FPGAs, ASIC accelerators

Âo Memory, I/O, storage, network accelerators

Âo Hardware/software co-design

Âo Efficient machine learning algorithms

Âo Approximate computing in machine learning

Âo Power/Energy and learning acceleration

Âo Software, library, and runtime for machine learning systems

Âo Workload scheduling and orchestration

Âo Machine learning in cloud systems

Âo Large-scale machine learning systems

Âo Emerging intelligent/cognitive system

Âo Converged/integrated infrastructure

Âo Machine learning systems for specific domains, e.g., financial, biological,

 Âeducation, commerce, healthcare

Authors of selected abstracts will be invited to give a 30-min presentation at the workshop.

ORGANIZERS

General Chair:Â Â Â Â Â Â Â Erik Altman

Program Committee Chairs:Â ÂZehra Sura, Parijat Dube

--

Best regardsï

Lu Li

Publicity Chair

University of Edinburgh

The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.