Dear Colleagues,

Non-functional constraints such as execution time, memory capacity, and energy consumption are a significant challenge for designers in the field of embedded systems. New applications are being proposed that integrate an increasing variety of functionality into everyday objects, imposing several additional requirements on embedded system designers, as follows:

• Increased computing workloads, elaborating and fusing multiple sensor data, even by advanced machine learning techniques;
• Reduced power consumption, allowing for smaller batteries and renewable power sources;
• Faster interaction with the environment, necessitating a high performance in data processing that is often reached by hardware implementations.

As an example, the physical dimensions and power consumption of embedded systems for the Internet of Things are frequently of interest. However, the need for small systems does not prevent higher demands for 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. Developers must grasp the nature of embedded systems architectures and strategies for extracting their full performance potential in this environment, as well as embedded design in general.

This Special Issue invites researchers to contribute original research, case studies, and reviews that address topics related to designs and applications of high-performance digital embedded systems.

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

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

Dr. Sergio Spanò
Prof. Dr. Gian Carlo Cardarilli
Guest Editors

Manuscript Submission Information

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• embedded systems
• digital electronics
• low-power
• IoT
• edge computing
• FPGA
• ASIC
• systems-on-chips
• machine learning