[hpc-announce] FastPath 2021 Call For Participations.

[Gan, Yiming]

CALL FOR PARTICIPATION

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FastPath'2021: International Workshop on Performance Analysis of Machine Learning Systems
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Sunday, March 28, 2021 – Virtual
14:50 - 20:30 UTC
10:50 am - 4:30 pm US EDT

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https://tinyurl.com/FastPath2021/Program<https://urldefense.proofpoint.com/v2/url?u=https-3A__tinyurl.com_FastPath2021_Program&d=DwMGaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=79oYwgpyyOBT1rfSOAp0mpbtUQ1E19PNLyH5xaOnAFk&m=7JD7YO0NImg10aSRclsN8eCWSz5zrPh_UvQzjgIiuHo&s=N4VWqKnRrB3MaljhkqMP_0wT1X16fbr0oMB7n2N2wgw&e=>
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FastPath 2021 is in conjunction with the IEEE ISPASS 2021 Conference:  http://ispass.org<https://urldefense.proofpoint.com/v2/url?u=http-3A__ispass.org_&d=DwMGaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=79oYwgpyyOBT1rfSOAp0mpbtUQ1E19PNLyH5xaOnAFk&m=7JD7YO0NImg10aSRclsN8eCWSz5zrPh_UvQzjgIiuHo&s=cbchZqQjCfdQai3Qa9XYsmhPrsE4ZjMvXQXXHEsSIgI&e=>
--> Registration required for access <--

FastPath 2021 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.


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PROGRAM

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| Time | Speaker | Affiliation | Talk Title |
| ---- | ------- | ----------- | ---------- |
| 10:50 - 11:00 | Organizers | | Opening Remarks |
| | | | |
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| 11:00 - 11:35 | Ana Klimovic | ETH-Zurich | Ingesting and Processing Data Efficiently |
| | | | for Machine Learning |
| | | | |
| 11:35 - 12:10 | Christian Kästner | Carnegie Mellon University | Toward a system-wide and interdisciplinary |
| | | | perspective on ML system performance |
| | | | |
| 12:10 - 12:45 | Kaoutar El Maghraoui | IBM Research | Hardware-aware Automated AI for Efficient |
| | | | Deep Learning across Hybrid Deployments: |
| | | | Current Landscape and Future Directions |
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| | | | |
| 12:45 - 13:45 | LUNCH / DINNER / BREAKFAST | | |
| | | | |
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| 13:45 - 14:20 | Siva Hari | NVidia | Resilience and Safety for Autonomous Vehicles |
| | | | |
| 14:20 - 14:55 | Amrita Mazumdar | Vignette AI / Univ of Washington | Learning for Better Video Processing Systems |
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| | | | |
| 14:55 - 15:15 | BREAK | | |
| | | | |
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| 15:15 - 15:50 | Martin Maas | Google Brain | A Taxonomy of Machine Learning for Systems Problems |
| | | | |
| 15:50 - 16:25 | Jongsoo Park | Facebook | Embedding Operations in Deep Learning |
| | | | Recommendation Models |
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| | | | |
| 16:25 - 16:30 | Organizers | | Closing Remarks |
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https://tinyurl.com/FastPath2021/Program<https://urldefense.proofpoint.com/v2/url?u=https-3A__tinyurl.com_FastPath2021_Program&d=DwMGaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=79oYwgpyyOBT1rfSOAp0mpbtUQ1E19PNLyH5xaOnAFk&m=7JD7YO0NImg10aSRclsN8eCWSz5zrPh_UvQzjgIiuHo&s=N4VWqKnRrB3MaljhkqMP_0wT1X16fbr0oMB7n2N2wgw&e=>

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ORGANIZERS

General Chair Erik Altman (IBM)

Program Chairs Parijat Dube (IBM)
Yuhao Zhu (Univ of Rochester)

Publicity Chair Yiming Gan (Univ of Rochester)

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PROGRAM COMMITTEE
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Deeksha Dangwal University of California - Santa Barbara
Christoph Dubach McGill University
Jingwen Leng Shanghai Jiao Tong University
Minsoo Rhu KAIST - Korea Advanced Institute of Science and Technology

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