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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
[Sender: Falk Pollok,
falk.pollok@ibm.com (with apologies for
multiple posts)]
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