Embedded systems have traditionally faced limitations in processing
speed and adaptability, particularly in real-time applications. Advances
in neural network acceleration offer a potential solution to these
constraints. Current embedded systems often struggle to handle
dynamic workloads efficiently, impacting performance in time-
sensitive applications. There is a need for a novel approach to enhance
processing capabilities without compromising real-time
responsiveness. This study introduces a novel Adaptive Neural
Acceleration Unit (ANAU) designed for 64-bit embedded systems. The
ANAU leverages adaptive neural networks to dynamically adjust
processing resources based on workload characteristics. The algorithm
was implemented on a state-of-the-art embedded platform and
evaluated across various real-time applications. The ANAU
demonstrated a 35% increase in processing speed and a 40% reduction
in power consumption compared to traditional methods. Real-time task
latency improved by 25%, with system stability maintained under high-
load conditions.
Sunil Kumar1, Akabarsaheb Babulal Nadaf2, Ankush M. Gund3, K. Amudha4, R.K. Parate5, Hakeem Ahmed Othman6
SGT University, India1, Abhijit Kadam Institute of Management and Social Sciences, India2, Bharati Vidyapeeth College of Engineering, India3, Kongunadu College of Engineering and Technology, India4, Seth Kesarimal Porwal College of Arts and Science and Commerce, India5, Albaydaa University, Republic of Yemen6
Embedded Systems, Adaptive Neural Acceleration, Real-Time Applications, Neural Networks, Processing Optimization
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| Published By :
ICTACT
Published In :
ICTACT Journal on Microelectronics
( Volume: 10 , Issue: 2 , Pages: 1790 - 1794 )
Date of Publication :
July 2024
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99
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