A Codesign Framework for the Development of Next Generation Wearable Computing Systems

Wearable devices can be developed using hardware platforms such as Application Specific Integrated Circuits (ASICs), Graphics Processing Units (GPUs), Digital Signal Processors (DSPs), Micro controller Units (MCUs), or Field Programmable Gate Arrays (FPGAs), each with distinct advantages and limitations. ASICs offer high efficiency but lack flexibility. GPUs excel in parallel processing but consume significant power. DSPs are optimized for signal processing but are limited in versatility. CPUs provide low power consumption but lack computational power. FPGAs are highly flexible, enabling powerful parallel processing at lower energy costs than GPUs but with higher resource demands than ASICs. The combined use of FPGAs and CPUs balances power efficiency and computational capability, making it ideal for wearable systems requiring complex algorithms in far-edge computing, where data processing occurs onboard the device. This approach promotes green electronics, extending battery life and reducing user inconvenience. The primary goal of this work was to develop a versatile framework, similar to existing software development frameworks, but specifically tailored for mixed FPGA/MCU platforms. The framework was validated through a real-world use case, demonstrating significant improvements in execution speed and power consumption. These results confirm its effectiveness in developing green and smart wearable systems.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2025-10-28
DOI
https://doi.org/10.3390/s25216624
Primary Topic
Green IT and Sustainability
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Codesign Framework for the Development of Next Generation Wearable Computing Systems

Raffaele Gravina, Fabio Frustaci, Francesco Porreca
Sensors
Green IT and Sustainability
article

A Codesign Framework for the Development of Next Generation Wearable Computing Systems

Raffaele Gravina, Fabio Frustaci, Francesco Porreca
article en

Abstract

Wearable devices can be developed using hardware platforms such as Application Specific Integrated Circuits (ASICs), Graphics Processing Units (GPUs), Digital Signal Processors (DSPs), Micro controller Units (MCUs), or Field Programmable Gate Arrays (FPGAs), each with distinct advantages and limitations. ASICs offer high efficiency but lack flexibility. GPUs excel in parallel processing but consume significant power. DSPs are optimized for signal processing but are limited in versatility. CPUs provide low power consumption but lack computational power. FPGAs are highly flexible, enabling powerful parallel processing at lower energy costs than GPUs but with higher resource demands than ASICs. The combined use of FPGAs and CPUs balances power efficiency and computational capability, making it ideal for wearable systems requiring complex algorithms in far-edge computing, where data processing occurs onboard the device. This approach promotes green electronics, extending battery life and reducing user inconvenience. The primary goal of this work was to develop a versatile framework, similar to existing software development frameworks, but specifically tailored for mixed FPGA/MCU platforms. The framework was validated through a real-world use case, demonstrating significant improvements in execution speed and power consumption. These results confirm its effectiveness in developing green and smart wearable systems.

SensorsVol. 25(21)
University of Calabria (IT)
Openalex Percentile: Top 69%
Green IT and Sustainability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.