[hpc-announce] FastPath 2018 - CALL FOR PAPERS

Guojing Cong gcong at us.ibm.com
Wed Feb 7 08:03:23 CST 2018

         FastPath 2018 - CALL FOR PAPERS
International Workshop on Performance Analysis of Machine Learning Systems
           April 2, 2018 - Belfast, Northern Ireland, United Kingdom
                        In conjunction with ISPASS 2018: 
FastPath 2018 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.
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:
o Workload characterization, performance modeling and profiling of machine
learning applications
o GPUs, FPGAs, ASIC accelerators
o Memory, I/O, storage, network accelerators
o Hardware/software co-design
o Efficient machine learning algorithms
o Approximate computing in machine learning
o Power/Energy and learning acceleration
o Software, library, and runtime for machine learning systems
o Workload scheduling and orchestration
o Machine learning in cloud systems
o Large-scale machine learning systems
o Emerging intelligent/cognitive system
o Converged/integrated infrastructure
o Machine learning systems for specific domains, e.g., financial, 
education, commerce, healthcare
Prospective authors must submit a 2-4 page extended abstract 
electronically at:
Authors of selected abstracts will be invited to give a 30-min 
presentation at the workshop.
Submission:                 March  1, 2018
Notification:               March 10, 2018
Final Materials / Workshop: April 2, 2018
General Chair:              Erik Altman
Program Committee Chairs: Zehra Sura, Parijat Dube
Publicity Chair:            Guojing Cong

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