CLOSED Call for Papers: Special Issue on Machine-Learning Architectures and Accelerators

IEEE Transactions on Computers seeks original manuscripts for a special issue on machine-learning architectures and accelerators, scheduled to appear in March 2020.
Share this on:
Submissions Due: 15 October 2019

In the era of big data, machine learning (ML) continues to play a critical role in extracting meaningful information out of the large amount of data that are generated every day. ML is fast becoming one of the most important pillars of the computing industry, as ML is extensively used in real-world applications at all scales–from datacenters to mobile/IoT devices. On the other hand, as we reach the end of Moore’s law and Dennard’s scaling, domain-specific architectures and emerging technologies are becoming essential to improve performance and energy-efficiency. These two trends have triggered the proliferation of specialized ML accelerators that can deliver orders of magnitude greater performance and energy-efficiency. The architecture community has not only explored new architectures for ML accelerators, but also put significant effort in applying emerging technologies (e.g., analog, memristor, spintronic, quantum, etc.) to accelerate ML workloads. This special issue of IEEE Transactions on Computers will explore academic and industrial research on all topics related to ML acceleration with specialized architectures and emerging technologies.

Topics of interest to this special issue include, but are not limited to:

  • New microarchitecture of hardware accelerators for ML
  • ML design methodologies for ML-centric or ML-aware hardware accelerators
  • New tools to design/build/optimize/debug the accelerated systems
  • ML workload acceleration on conventional technologies such as GPU, FPGA, and ASIC
  • Acceleration of new ML algorithms
  • Model compression and acceleration
  • Acceleration for edge computing and IoT
  • ML acceleration in data center for cloud computing
  • ML acceleration with emerging technologies
  • Comparison studies of ML acceleration using conventional and emerging technologies

Submitted articles must not have been previously published or currently submitted for journal publication elsewhere. As an author, you are responsible for understanding and adhering to the submission guidelines. Please thoroughly read these before submitting your manuscript. Please submit your paper at

Please note the following important dates:

• Submission Deadline: CLOSED
• Reviews Completed: December 1, 2019
• Major Revisions Due: January 1, 2020
• Reviews of Revisions Completed: January 15, 2020
• Notification of Final Acceptance: February 1, 2020
• Publication Materials for Final Manuscripts Due: February 15, 2020
• Publication: March 2020

Please address all correspondence regarding this special issue to Lead Guest Editor Xuehai Qian (

Guest Editors

Xuehai Qian
Dept. of ECE, University of Southern California

Yanzhi Wang
Dept. of ECE, Northeastern University

Corresponding Topical Editor

Prof. Avinash Karanth
Electrical Engineering and Computer Science
Ohio University