Power Optimization Approaches in Mobile Operating Systems

Shrinking feature sizes, reduced voltages, and higher transistor count of nano-scale silicon chips challenge designers in terms of performance, power consumption, and reliability. This paper investigates the effect of simultaneous use of dynamic voltage and frequency scaling (DVFS) and body biasing (BB) on power consumption, reliability, and performance. An analytical model of reliability as a function of body bias voltage, supply voltage, and frequency is proposed. We derive a three dimensional optimization problem by exploiting proposed reliability model in conjunction with power consumption and performance model. The resulting problem is solved using widely-used geometric optimization to identify optimal supply voltage and body bias voltage and then is validated using accurate simulation. Afterwards, it is demonstrated how joint energy-performance-reliability space optimization method can be used in an adaptive reliability-aware power management systems. Finally, we show that combined soft error aware BB and DVFS is capable of improving power consumption about 30% in comparison to reliability-aware DVFS only for the same level of reliability and performance constraints.

Nowadays, power optimization has become a major interest for most digital hardware designers. Some, traditionally, might stick to offline power estimation especially in early design phases; some others resort to the modern and very promising runtime power management. Therefore, the Online Power Monitoring (OPM) is considered as an important feature serving real-time power optimization. OPM favors both in-situ power estimation and subsequent prediction, and can be exploited by the Dynamic Voltage and Frequency Scaling (DVFS) mechanism. DVFS, a modern technique for digital circuits power optimization, provides a realtime and adaptive voltage and/or frequency tuning while securing the systems’ performance and integrity. In this paper, we present and evaluate an accurate online power monitoring methodology of FPGA IPs. We estimate power consumption using machine learning techniques, based on the IP’s most power-influential operating modes and its inputs activity characteristics. The proposed online monitoring mechanism drastically reduces the communication-derived latency between the monitor and the DVFS. Experimental results show a mean absolute percentage error below $$1.5\%$$ for the estimated power consumption.

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

Modern data centers must provide performance assurance for complex system software such as multi-tier web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. Various power-efficient performance management strategies have been proposed based on dynamic voltage and frequency scaling (DVFS). Virtualization technologies have also made it possible to consolidate multiple virtual machines (VMs) onto a smaller number of active physical servers for even greater power savings, but at the cost of a higher overhead. This paper proposes a performance-controlled power optimization solution for virtualized data centers with multi-tier applications. While existing work relies on either DVFS or server consolidation in a separate manner, our solution utilizes both strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the data center level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Empirical results on a hardware testbed demonstrate that our solution can effectively achieve performance-assured power savings. Extensive simulation results, based on a trace file of 5,415 real servers, demonstrate the efficacy of our solution in large-scale data centers.

Dynamic voltage and frequency scaling (DVFS) is a means to adjust the computing capacity and power consumption of computing systems to the application demands. DVFS is generally useful to provide a compromise between computing demands and power consumption, especially in the areas of resource-constrained computing systems. Many modern processors support some form of DVFS. In this article, we focus on the development of an execution framework that provides lightweight DVFS support for reactive stream processing systems (RSPS). RSPs are a common form of embedded control systems, operating in direct response to inputs from their environment. At the execution framework, we focus on support for many-core scheduling for parallel execution of concurrent programs. We provide a DVFS strategy for RSPs that is simple and lightweight, to be used for dynamic adaptation of the power consumption at runtime. The simplicity of the DVFS strategy became possible by the sole focus on the application domain of RSPs. The presented DVFS strategy does not require specific assumptions about the message arrival rate or the underlying scheduling method. While DVFS is a very active field, in contrast to most existing research, our approach works also for platforms like many-core processors, where the power settings typically cannot be controlled individually for each computational unit. We also support dynamic scheduling with variable workload. While many research results are provided with simulators, in our approach, we present a parallel execution framework with experiments conducted on real hardware, using the single-chip cloud computer many-core processor. The results of our experimental evaluation confirm that our simple DVFS strategy provides potential for significant energy saving on RSPs.

<title>Abstract</title> This work presents an AI-enabled low-power UART (Universal Asynchronous Receiver Transmitter) architecture optimized for advanced automotive applications using a hybrid front-end and back-end design approach. The front-end design and functional verification are carried out in Vivado, while the back-end physical implementation and power optimization are performed using the Sky Water 130 nm PDK technology. The proposed design integrates artificial intelligence–based optimization strategies to enhance communication efficiency and minimize total power dissipation. Machine learning techniques, including reinforcement learning and regression-based prediction, are employed to analyse switching activity and workload behaviour, enabling real-time adaptive control of key operating parameters such as voltage, frequency, and clock gating. By incorporating dynamic voltage and frequency scaling (DVFS) and adaptive clock gating, the architecture achieves notable reductions in both dynamic and static power while maintaining high data integrity. The AI-assisted UART intelligently manages buffer utilization and module activation based on data traffic patterns, leading to superior energy efficiency and performance balance. Simulation and physical verification demonstrate significant improvements in power, latency, and area compared to conventional UART architectures. The proposed architecture provides a scalable, energy-efficient, and intelligent communication interface, ideally suited for next-generation automotive embedded and sensor systems requiring low power and high reliability.

The functionality of embedded systems is ever growing. The computational power of embedded systems is growing to match this demand, with embedded multiprocessor systems becoming more common. The limitations of embedded systems are not always related to chip size but are commonly due to energy and/or power constraints. While it can be possible to embed a more powerful Multiprocessor System on Chip (MPSoC), it is not always possible to provide an energy or power supply that meets its demands within the device’s size and weight requirements. Power management through Dynamic Voltage and Frequency Scaling (DVFS) enables the device to be run at less than its maximum voltage and frequency, allowing high computational capability when necessary while conserving power at other times. Embedded systems commonly perform real-time functionality. A real-time application has an associated formal model to verify that it meets its timing requirements. This formal model is used to perform a worst-case timing analysis to ensure that the application meets its requirements. These models incorporate the worst-case timing of the application’s computation and communication. Timing changes due to power management must also be taken into account, complicating the verification process. The drive for evermore functionality has led to mixed time-criticality systems, in which multiple applications of various timing criticalities share the same (hardware) resources. This complicates the verification process further as the timing interference due to shared resource contention must be taken into account. A monolithic verification effort is therefore traditionally required after system integration and must be carried out again if any modifications are made that affect the timing of any of the applications. The problem that we aim to solve in this work is to enable real-time applications to perform independent execution and power management without violating their timing requirements or invalidating the timing verification of concurrently executing applications. To solve this problem, we contribute the Composable and Predictable Microkernel (CoMik) to composably and predictably virtualise processors. When used in combination with composable and predictable memory controllers and interconnect (as provided by the Composable and Predictable System-on-Chip (CompSOC) platform), these virtual processors cannot interfere with each other’s timing by even a single cycle. If whatever executes on the virtual processors (e.g. an Operating System (OS) or an application directly) has a real-time requirement, it can be verified independently of whatever executes on the concurrent virtual processors and other virtual resources. To enable formally analysable application execution, we contribute the Predictable Operating System (POSe) that enables dataflow applications to be executed on the (virtualised) processor. We contribute a combined application and platform dataflow graph, including an algorithm to automate this process. When annotated with worst-case timings, the combined application and system graph is then used to verify that the application meets its timing requirement. If the application’s performance is better than its requirement (e.g. when the input or platform behaviour are better than worst case), its performance can be reduced using DVFS to achieve a reduction in power consumption. We contribute an off-line convex optimisation that uses the combined application and platform dataflow model to derive static run-time operating frequency levels to achieve low power consumption. The off-line technique is able to exploit static slack in the schedule, but not dynamic run-time slack due to variations in task execution times. Before dynamic slack can be used it must be possible to observe it. For this purpose, CoMik provides independent power, energy and timing accounting per virtual processor. This enables each virtual processor to be assigned individual power and energy budgets and POSe applications to be assigned timing budgets. We contribute a description and model of how energy and power budgets can be distributed between multiple virtual processors, enabling whatever executes on the virtual processor to perform composable independent power-management without affecting the ability of other virtual processors from using their entire budget allocation. Using CoMik’s accounting infrastructure, we also demonstrate how the quality of applications can be dynamically scaled to assist meeting timing, energy or power requirements. We further contribute a distributed dynamic power management policy that enables dataflow applications that are mapped onto multiple (virtual) processors to make distributed dynamic slack observations and local power-management decisions. We demonstrate the applicability of the presented techniques on an implemented Field Programmable Gate Array (FPGA) prototype of a CompSOC hardware platform instance, using an H.263 decoder as a case-study application. We show that our techniques do not only work in theory, but that they are also implementable and implemented.

Dynamic voltage and frequency scaling (DVFS) is commonly employed on modern superscalar processors to reduce energy when peak performance is not needed or allowed. As technology scales, the effectiveness of DVFS is limited by the shrinking viable supply voltage range. This work proposes dynamic core scaling (DCS) to extend performance-energy tradeoff capabilities in superscalar processors. DCS ensures that programs run at a given percentage of their maximum speed and, at the same time, minimizes energy consumption by dynamically adjusting the active superscalar datapath resources. Evaluations using an 8-way superscalar processor implemented on 45nm circuit infrastructure show that DCS is more effective in performance-energy tradeoffs than DVFS at the high performance end. When used together with DVFS, DCS saves an additional 20% of a full-size core's energy on average. At the minimum operating voltage, DVFS stops reducing energy, while DCS is still able to achieve an average of 46% further energy reduction.

Dynamic Voltage and Frequency Scaling (DVFS) approach is proposed to control the power consumption in devices of different types like from a small mobile device to a huge server. This paper aims to study the effect of parallel execution of two benchmark applications, one the Stanford Single Source benchmarks (lightweight parallel code) and, Stanford Parallel Applications for Shared Memory (SPLASH-2) programs (heavy real-time data streaming parallel applications that run on multi -core clusters) on DVFS for dark silicon ameliorating. Both these benchmarks executed on a gem5 Full System simulator, booting a linaro based linux kernel. To get a holistic big picture of the power and thermal properties of systems that run on DVFS, in addition to the control feature present in gem 5, a power-estimation framework used for the evaluation of the efficiency of various DVFS policies, the MCPAT and Hotspot is also employed. Based on the execution of Stanford workloads DVFS model is analyzed in terms of memory footprint and on other critical processor performance metrics, like running time per core, bus latency, number of read, writes, access time, cache hit or miss. The gem5 is extended with full DVFS support by the addition of a framework in which easy power-model integration can be done.

Dynamic Voltage and Frequency Scaling (DVFS) is widely used in today's mobile devices. Commonly adopted OS level DVFS policies usually operate with high sampling and adjustment frequency in order to compensate for the lack of information from applications. In this paper, we consider the application of video decoding and propose a method that enables DVFS at a coarse time granularity by taking advantage of application level semantics. Using machine learning, we build a prediction model for the choice of CPU frequency based on the estimated workload and the progress of execution. We show that with our method, the system is able to perform DVFS with a frequency of only around 1 Hz, abiding by the practical constraints imposed by existing operating systems, but still saves an average of 40.1% in energy consumption compared with the execution at nominal speed. Further, we argue that the law of diminishing returns applies if we increase the frequency at which DVFS operates, and thus it may be costly and impractical to perform application level DVFS at a very fine time granularity.

Recent advancements in deep learning have significantly increased AI processors' energy consumption, which is becoming a critical factor limiting AI development. Dynamic Voltage and Frequency Scaling (DVFS) stands as a key method in power optimization. However, due to the latency of DVFS control in AI processors, previous works typically apply DVFS control at the granularity of a program's entire duration or sub-phases, rather than at the level of AI operators.

Performance-controlled server consolidation for virtualized data centers with multi-tier applications

Graphics processor (GPU) architectures have evolved rapidly in recent years with increasing performance demanded by 3D graphics applications such as games. However, challenges exist in integrating complex GPUs into mobile devices because of power and energy constraints, motivating the need for energy efficiency in GPUs. While a significant amount of power optimisation research effort has concentrated on the CPU system, GPU power efficiency is a relatively new and important area because the power consumed by GPUs is similar in magnitude to CPU power. Power and energy efficiency can be introduced into GPUs at many different levels: 1) Hardware component level - queue structures, caches, filter arithmetic units, interconnection networks, processor cores, etc., can be optimised for power. 2) Algorithm level - the deep and complex graphics processing computation pipeline can be modified to be energy aware. Shader programs written by the user can be transformed to be energy aware. 3) System level - co-ordination at the level of task allocation, voltage and frequency scaling, etc., requires knowledge and control of several different GPU system components We outline two strategies for applying energy optimisations at different levels of granularity in a GPU: (1) Texture Filter Memory (TFM) - an energy-efficient addition to the texture memory hierarchy component; and (2) an overall system level Dynamic Voltage and Frequency Scaling (DVFS) based energy reduction strategy based on an accurate tile-level slack prediction. Texture Filter Memory is an augmentation of the standard GPU texture cache hierarchy. Instead of a regular data cache hierarchy, we employ a small first level register based structure that is optimised for the relatively predictable memory access stream in the texture filtering computation. Predictability of the memory access patterns leads us to reduce power by using a simpler register access structure. Power is saved by avoiding the expensive tag lookup and comparisons present in regular caches. Further, the texture filter memory is a very small structure, whose access energy is much smaller than a data cache of similar performance. Dynamic Voltage and Frequency Scaling, an established energy management technique, can be applied in GPUs by first predicting the workload in a given frame, and, where sufficient slack exists, lowering the voltage and frequency levels so as to save energy while still completing the work within the frame rendering deadline. We apply DVFS in a tiled graphics renderer, where the workload prediction and voltage/frequency adjustment is performed at a tile-level of granularity, which creates opportunities for on-the-fly correction of prediction inaccuracies, ensuring high frame rates while still delivering low power. Tile-level energy management schemes are distinctly superior to schemes that work at the granularity of the entire frame in terms of energy saving and quality of the rendering measured in terms of frames rendered per second. The prediction mechanism relies both on the history - workload observed for the tile in recent frames, as well as rank - a quantification of the tile workload based on the frame structure, consisting of geometry, pixel shading, texture, and raster operations workloads.

Multi-core processors have become an integral part of mainstream high performance computer systems. In parallel, exponentially increasing power density and packaging costs have necessitated system level thermal management solutions for multi-core systems. Dynamic thermal management (DTM) techniques monitor on-chip temperature continuously and typically employs dynamic voltage and frequency scaling (DVFS) to lower the temperature when it exceeds a pre-defined threshold. State-of-the-art DTM solutions for multi-core systems include distributed DVFS (where each core can scale the voltage/frequency individually) and global DVFS (where all cores scale voltage/frequency simultaneously). Distributed DVFS generally offers higher performance than global DVFS, but it is hard to implement and has major scalability issues.

With growing integration, power consumption is becoming a major issue for multi-core chips. At system level, per-core Dynamic Voltage and Frequency scaling (DVFS) is expected to save substantial energy provided an adapted control. In this paper we propose a local on-line optimization technique to reduce energy in data-flow architecture, thanks to a Local Power Manager (LPM) using Vdd-Hopping for efficient local DVFS. The proposed control is a hybrid global and local scheme which respects throughput and latency constraints. The approach has been fully validated on a real Multiple Input Multiple Output (MIMO) Telecom application using a SystemC platform instrumented with power estimates. Local DVFS brings 45% power reduction compared to idle mode. When local on-line optimization benefit from computation time variations, 30% extra energy savings can be achieved.