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

[Falk Pollok]

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 2020 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

[Sender: Falk Pollok, fpkibm<.at>gmail.com (with apologies for multiple posts)]