The rapid evolution of electronic embedded systems (EES) has brought
significant challenges in optimizing their performance in real-time
environments. These systems are often deployed in critical applications,
such as automotive, medical, and IoT devices, where efficient resource
management and adaptive decision-making are essential for optimal
performance. Traditional optimization methods struggle to meet the
dynamic and complex demands of modern embedded systems. As the
complexity of electronic embedded systems increases, ensuring real-
time performance while minimizing energy consumption, latency, and
operational costs becomes more difficult. Static configurations or
conventional algorithms cannot adapt quickly to changing conditions,
leading to suboptimal performance. This problem is further
exacerbated by the need for fast decision-making within limited
computational resources. This study proposes using Deep
Reinforcement Learning (DRL) algorithms to optimize the real-time
performance of electronic embedded systems. DRL leverages an agent-
based approach to autonomously learn optimal strategies through trial
and error in dynamic environments. The proposed method involves
training a DRL model to intelligently manage system resources, adjust
parameters, and enhance decision-making in real-time based on
feedback from the system’s environment. Key DRL techniques, such as
Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), are
utilized to train agents in various system scenarios. The results show
that DRL-based optimization significantly improves system efficiency,
leading to reduced latency, enhanced throughput, and optimized power
consumption without compromising the system’s responsiveness. The
proposed method outperforms traditional optimization approaches,
particularly in highly dynamic and resource-constrained
environments, by enabling continuous adaptation to changing operational conditions.
N. Jagadeeswari1, R. Sudha2, M. Bhavani3
Thanthai Periyar Government Institute of Technology, India1,2, Government College of Engineering, Srirangam, India3
Deep Reinforcement Learning, Electronic Embedded Systems, Real- Time Optimization, Power Consumption, System Performance
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| Published By :
ICTACT
Published In :
ICTACT Journal on Microelectronics
( Volume: 10 , Issue: 4 , Pages: 1909 - 1916 )
Date of Publication :
January 2025
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33
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