[AISWorld] FastPath 2020: International Workshop on Performance Analysis of Machine Learning Systems

[Falk Pollok]

Reminder: Submission for FastPath2020 ends in one week.

FastPath 2020:  International Workshop on Performance Analysis of Machine Learning Systems
(An ISPASS Workshop under the auspices of IEEE)

April 5, 2020 - Boston, Massachusetts, United States
https://fastpath2020.github.io in conjunction with ISPASS 2020: http://www.ispass.org/ispass2020

SUMMARY
FastPath  2019 brings together researchers and practitioners involved in  crossstack 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:

    - Workload characterization, performance modeling and profiling of machine learning applications
    - GPUs, FPGAs, ASIC accelerators
    - Memory, I/O, storage, network accelerators
    - Hardware/software co-design
    - Efficient machine learning algorithms
    - Approximate computing in machine learning
    - Power/Energy and learning acceleration
    - Software, library, and runtime for machine learning systems
    - Workload scheduling and orchestration
    - Machine learning in cloud systems
    - Large-scale machine learning systems
    - Emerging intelligent/cognitive system
    - Converged/integrated infrastructure
    - Machine learning systems for specific domains, e.g., financial, biological, education, commerce, healthcare


SUBMISSION
Prospective authors must submit a 2-4 page extended abstract: https://easychair.org/conferences/?conf=fastpath2020

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

KEY DATES
Submission: February 21, 2020            Notification: March 2, 2020            Final Materials / Workshop: April 5, 2020

ORGANIZERS
General Chair: Erik Altman            Program Committee Chairs:  Parijat Dube, Vijay Janapa Reddi