Deadline extended to March 24
Background
FastPath 2023 will be held with the ISPASS Conference in Raleigh, NC and
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 analysis and evaluation 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 systems
- Converged/integrated infrastructure
- Machine learning systems for specific domains, e.g.,
financial, biological, education, commerce, healthcare
Key
Dates
|
Item |
Date |
|
Submission |
March 24, 2023 (Extended from March 17) |
|
Notification |
April 4, 2023 |
|
Workshop |
April 23, 2023 |
Submission
Prospective authors must submit a
2-4 page extended abstract electronically on EasyChair