Special Issue on Applications, Architectures, Methods and Tools for Machine- and Deep Learning (AMDL)
• 大类 : 工程技术 - 4区
• 小类 : 计算机：硬件 - 4区
• 小类 : 计算机：理论方法 - 4区
• 小类 : 工程：电子与电气 - 4区
This Special Issue of the MICROPORCESSORS AND MICROSYSTEMS (MICPRO) journal will be based on selected high-quality papers from the Applications, Architectures, Methods and Tools for Machine- and Deep Learning (AMDL) special session from the Euromicro Conference on Digital system Design (DSD) 2018, as well as, other high-quality papers targeting the subject of this Special Issue and submitted in reaction to this Call for Papers.
Machine learning has numerous important applications in intelligent systems within many areas, like automotive, avionics, robotics, health-care, well-being, and security. The recent progress in Machine Learning (ML), and particularly in Deep Learning (DL), has dramatically improved the state-of-the-art in object detection, classification and recognition, and in many other domains. Whether it is superhuman performance in object recognition or beating human players in Go, the astonishing success of DL is achieved by deep neural networks. However, the complexity of DL for many practical applications can be huge, and their processing may demand a high computing effort and excessive energy consumption. Their training requires big data sets, making the training even orders of magnitude more intensive than the already very demanding inference phase.
For this Special Issue we encourage you to submit papers related to advanced applications, architectures, methods and tools for ML and DL. Notice that extended versions of the DSD 2018 papers must contain at least 30% of new material different from the original work published in the AMDL 2018 special session. Extended and new papers related (but not limited) to the following topics are considered for selection by a review:
Architectural support for ML and DL, with emphasis on energy reduction, computation efficiency and/or computation flexibility, both for inference and/or for learning
Spiking and brain-inspired neural networks and their implementation
Efficient mapping of ML and DL applications to target architectures, including many-core, GPGPU, SIMD, FPGA, and HW accelerators
New learning approaches for ML and DL, with emphasis on e.g. faster and more efficient learning, online learning, and quality of learning
High-level programming language support for ML and DL
ML and DL for design automation
Tools and frameworks for ML and DL
Using of approximate computing to decrease the energy demands of ML and DL