Optimizing Power & Reliability in Mobile Computing with DVFS

<p>Low power mobile computing systems such as smartphones and wearables have become an integral part of our daily lives and are used in various ways to enhance our daily lives. Majority of modern mobile computing systems are powered by multi-processor System-on-a-Chip (MPSoC) where multiple processing elements are utilized on a single chip. Given the fact that these devices are battery operated most of the times thus have limited power supply and the key challenges include catering for performance while reducing the power consumption. Moreover the reliability in terms of lifespan of these devices are also affected by the peak thermal behaviour on the device which retrospectively also make such devices vulnerable to temperature side-channel attack. This book is concerned with performing Dynamic Voltage and Frequency Scaling (DVFS) on different processing elements such as CPU & GPU and memory unit such as RAM to address the aforementioned challenges. Firstly we design a Computer Vision based machine learning technique to classify applications automatically into different categories of workload such that DVFS could be performed on the CPU to reduce the power consumption of the device while executing the application. Secondly we develop a reinforcement learning based agent to perform DVFS on CPU and GPU while considering the user's interaction with such devices to optimize power consumption and thermal behaviour. Next we develop a heuristic based automated agent to perform DVFS on CPU GPU and RAM to optimize the same while executing an application. Finally we explored the affect of DVFS on CPUs leading to vulnerabilities against temperature side-channel attack and hence we also designed a methodology to secure against such attack while improving the reliability in terms of lifespan of such devices.</p><p> </p><p>This book is based on the doctoral thesis titled "Novel DVFS Methodologies For Power-Efficient Mobile MPSoC."<br>Cite: Dey Somdip (2023) <em>Novel DVFS Methodologies For Power-Efficient Mobile MPSoC</em>. Doctoral thesis University of Essex.</p>