Event Description: The workshop will bring together software developers, scientists, academia, and industry luminaries to share learnings around the integration and use of FPGA devices in HPC, Data Analytics, and Artificial Intelligence (Machine Learning and Deep Learning) workloads in Intel-based HPC systems. The workshop will cover strategies for migrating workloads onto FPGAs, performance comparisons, aspects of productivity, performance portability, and scalability. Objectives of the workshop: 1.) using FPGAs on Intel-based systems and scientific achievements, 2.) FPGA-related trends and challenges, and 3.) foster collaborations and strengthen the community of FPGA enthusiasts using Intel-based HPC systems.

Topics of Interest are (but not limited to): Sharing field-programmable gate array (FPGA), FPGA programming environments and abstraction tools, FPGA programming models, comparison of FPGA performance vs traditional or GPU hardware, embedded memory architectures, algorithmic techniques and mapping to FPGAs, machine learning algorithms on FPGAs, FPGA offload techniques, FPGA interfaces to traditional hardware, integrating FPGAs into an existing HPC environment.

Keywords: field-programmable gate array (FPGA), reconfigurable architectures, HPC, Artificial Intelligence (machine learning and deep learning) applications performance, FPGAs using Intel-based technology, benchmarking, programming environments, FPGA tools and techniques.

Instructions for Papers:
- Length rules: minimum 6 pages, maximum 12 pages.
- The accepted papers will be published in the Springer's Lecture Notes in Computer Science (LNCS) series.
- Manuscripts should be therefore prepared following the publisher's guidelines for authors, and using the publisher templates.
- See the workshop website with links to Submission page (EasyChair) and guidelines for authors.