CLOSED Call for Papers: Special Issue on Machine Learning Acceleration
IEEE Micro seeks submissions for this special issue.
 

Submissions due: CLOSED

  • Initial notifications: May 22, 2019
  • Revised papers due: June 21, 2019
  • Final notifications: June 28, 2019
  • Final versions due: July 12, 2019
  • Publication date: Sept/Oct 2019

In recent years, machine learning (ML) has become one of the most important pillars of computing industry, driven by the remarkable advances in the theory and their extensive use in real-world applications. To accomplish the phenomenal success, research and industry communities have exploited acceleration solutions, which deliver orders-of-magnitude greater performance and efficiency by specializing hardware and software for ML. As the importance of ML in the emerging applications increases, the ML accelerators have become the critical component of every modern computing system–from data centers to mobile/IoT devices. The community has not only extensively explored new architectures to improve the performance and efficiency of these accelerators, but also put significant effort on raising usability and programmability by offering programming models, high-level language, compiler, runtime software, and tools. This special issue of IEEE Micro will explore academic and industrial research on all topics, which relate to hardware and software acceleration solutions, specialized for ML. Such topics include, but are not limited to:

  • New design methodologies for ML-centric or ML-aware hardware accelerators
  • New microarchitecture designs of hardware accelerators for ML
  • ML workload acceleration on existing accelerators such as GPU, FPGA, CGRA, or ASIC
  • New compiler and optimization techniques for ML acceleration
  • New tools to design/build/optimize/debug the accelerated systems
  • New ML modeling, optimization, quantization, and compression for acceleration
  • Acceleration for new ML algorithms
  • ML acceleration for edge computing and IoT
  • ML acceleration for cloud computing
  • Comparison studies of different acceleration techniques
  • Survey and tutorial studies of ML acceleration

Submission guidelines

Please see the Author Information page and the peer review page for more information. Please submit electronically through ScholarOne Manuscripts, selecting this special-issue option.

Questions?

Contact the guest editors at mi5-2019@computer.org (Hadi Esmaeilzadeh and Jongse Park) or the editor-in-chief Lizy John (ljohn@ece.utexas.edu)

Special Issue on Machine Learning Acceleration
19 April 2019