Hardware Acceleration for Machine Learning, 2nd Edition

Dear Colleagues,

Non-functional constraints such as execution time, memory capacity, and energy consumption pose significant challenges for designers in the field of machine learning systems. New applications are being proposed that integrate various functionalities into everyday objects, imposing several additional requirements on embedded system designers, such as the following:

• Increased computing workloads when elaborating and fusing multiple data, even when using advanced machine learning techniques;
• Reduced power consumption, allowing for smaller batteries and renewable power sources;
• Faster interaction with the environment, requiring a high level of data processing performance often reached using hardware implementations.

For example, the physical dimensions and power consumption of embedded Internet of Things systems are frequently of interest. However, the need for small systems does not prevent greater demands from being made with regard to functionality and speed. Simultaneously, designers must respond to a growing need for more powerful edge systems capable of managing vast fleets of connected devices while running resource-intensive algorithms such as sensor fusion, feedback control, and machine learning algorithms. Developers must understand the nature of hardware architectures and strategies for extracting their full performance potential in this environment as well as embedded design in general.

For this Special Issue, we invite researchers to contribute original research, case studies, and reviews that address topics related to the design and application of hardware accelerators for machine learning.

The topics relevant for this Special Issue include (but are not limited to) the following:

• Low-power IoT applications;
• Embedded FPGA and SoC implementations;
• Embedded ASIC implementations;
• Machine learning on Edge;
• Efficient data processing algorithms.

Dr. Sergio Spanò
Prof. Dr. Gian Carlo Cardarilli
Dr. Luca Di Nunzio
Guest Editors

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