March 28, 2021 - Virtual(Approximate 9:00 am - 5:00 pm, US EDT = 13:00 - 21:00 UTC)

https://tinyurl.com/fastpath2021 in conjunction with ISPASS 2021: http://www.ispass.org/ispass2021

SUMMARY
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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
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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
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Prospective authors must submit a 2-4 page extended abstract: https://easychair.org/conferences/?conf=fastpath2021

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

KEY DATES
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Submission: February 15, 2021 Notification: March 1, 2021 Final Materials / Workshop: March 28, 2021

ORGANIZERS
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General Chair: Erik Altman Program Committee Chairs: Parijat Dube, Yuhao Zhu.