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Call for Papers:
FASTPATH WORKSHOP: International Workshop on Performance Analysis
of Machine Learning Systems
https://tinyurl.com/fastpath2021<https://urldefense.proofpoint.com/v2/url?u=https-3A__tinyurl.com_fastpath2021&d=DwMGaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=79oYwgpyyOBT1rfSOAp0mpbtUQ1E19PNLyH5xaOnAFk&m=H5k1q5kZevy_daDFiNpWImqV7Lxjs_vsLugHy8qBeYg&s=eThLNJ2JzOphH7OiwVaUviiYyicTyi_r8YiWsnQML3c&e=>
March 28, 2021 – Virtual (Approximately 9:00 am - 5:00 pm US EDT =
13:00 - 21:00 UTC)
in conjunction with ISPASS 2021:
http://www.ispass.org/ispass2021<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.ispass.org_ispass2021&d=DwMGaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=79oYwgpyyOBT1rfSOAp0mpbtUQ1E19PNLyH5xaOnAFk&m=H5k1q5kZevy_daDFiNpWImqV7Lxjs_vsLugHy8qBeYg&s=NsROXLHVsNmfZurPvy_B7rOrxTNc60Lo93B07kvXaCs&e=>
SUMMARY
FastPath 2020 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:
- 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=fastpath2021<https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dfastpath2021&d=DwMGaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=79oYwgpyyOBT1rfSOAp0mpbtUQ1E19PNLyH5xaOnAFk&m=H5k1q5kZevy_daDFiNpWImqV7Lxjs_vsLugHy8qBeYg&s=zejHrcz-hLNrTIFWzXUrsJ2ROMT9eE07TetlmgvHbdk&e=>
Authors of selected abstracts will be invited to give a 30-min
presentation at the workshop.
KEY DATES
Submission: February 26, 2021
Notification: March 8, 2021
Final Materials / Workshop: March 28, 2021
ORGANIZERS
General Chair: Erik Altman
Program Committee Chairs: Parijat Dube, Yuhao Zhu
Publicity Chair: Yiming Gan
_______________________________________________
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