Cooperative Power and Resource Management for Heterogeneous Mobile Architectures

Heterogeneous architectures have been ubiquitous in mobile system-on-chips (SoCs). The demand from different application domains such as games, computer vision and machine learning which requires massive parallelism of computation has driven the integration of more accelerators into mobile SoCs to provide satisfactory performance energy-efficiently. These on-chip computing resources typically have their individual runtime systems including: (1) a software governor: continuously monitors hardware utilization and makes decisions of trade-off between performance and power consumption. (2) software stack: allows application developers to program the hardware for general purpose computation and perform memory management and profiling. As computation of mobile applications may demand all sorts of combinations of computing resources, we identify two problems: (1) individual runtime can often lead to poor performance-power trade-off or inefficient utilization of computing resources. (2) existing approaches fail to schedule subprograms among different computing resources and further lose the opportunity to avoid resource contention to gain better performance

Impact of Memory Frequency Scaling on User-centric Smartphone Workloads

Improving battery life in mobile phones has become a top concern with the increase in memory and computing requirements of applications with tough quality-of-service needs. Many energy-efficient mobile solutions vary the CPU and GPU voltage/frequency to save power consumption. However, energy-aware control over the memory bus connecting the various on-chip subsystems has had much less interest. This measurement-based study first analyse the CPU, GPU and memory cost (i.e. product of utilisation and frequency) of user-centric smartphone workloads. The impact of memory frequency scaling on power consumption and quality-of-service is also measured. We also present a preliminary analysis into the frequency levels selected by the different default governors of the CPU/GPU/memory components.We show that an interdependency exists between the CPU and memory governors and that it may cause unnecessary increase in power consumption, due to interference with the CPU frequency governor. The observations made in this measurement-based study can also reveal some design insights to system designers

Characterization of smartphone governor strategies and making of a workload aware governor

This thesis presents the importance of workload characterization towards governing the operational voltage and frequency of a smartphone processor by running a series of workload on an ARM v8 processor. The idea of finishing a task as fast as possible to return to idle state(race-to-idle) versus the idea of choosing the correct frequency for time deltas(pace-to-idle) is studied in detail. Android governors either statically use a single frequency for the entire active time or determines the voltage and frequency dynamically based on the load average on the processor. Similar load averaging strategies are used for other blocks in SoC (System on Chip) like the GPU or the media processor. However, the different blocks of a SoC draw power from the same current source. Owing to lack of fine-grained workload characterization, the power is redirected to the not-so-important unit providing poor performance and energy efficiency. The behavior of different existing governors is explored by running on a variety of workload and analyze the optimal strategy for energy efficiency satisfying an acceptable user performance. Crucial traits of active user applications are inferred from scheduler to fine tune the optimal voltage and frequency across different blocks under constrained power source to build a system-wide governor.Electrical and Computer Engineerin

CPU-GPU-Memory DVFS for Power-Efficient MPSoC in Mobile Cyber Physical Systems

Most modern mobile cyber-physical systems such as smartphones come equipped with multi-processor systems-on-chip (MPSoCs) with variant computing capacity both to cater to performance requirements and reduce power consumption when executing an application. In this paper, we propose a novel approach to dynamic voltage and frequency scaling (DVFS) on CPU, GPU and RAM in a mobile MPSoC, which caters to the performance requirements of the executing application while consuming low power. We evaluate our methodology on a real hardware platform, Odroid XU4, and the experimental results prove the approach to be 26% more power-efficient and 21% more thermal-efficient compared to the state-of-the-art system

Power-Performance Modeling and Adaptive Management of Heterogeneous Mobile Platforms​

abstract: Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power consumption under many application scenarios. Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Predictive Thermal Management for Energy-Efficient Execution of Concurrent Applications on Heterogeneous Multicores

Current multicore platforms contain different types of cores, organized in clusters (e.g., ARM's big.LITTLE). These platforms deal with concurrently executing applications, having varying workload profiles and performance requirements. Runtime management is imperative for adapting to such performance requirements and workload variabilities and to increase energy and temperature efficiency. Temperature has also become a critical parameter since it affects reliability, power consumption, and performance and, hence, must be managed. This paper proposes an accurate temperature prediction scheme coupled with a runtime energy management approach to proactively avoid exceeding temperature thresholds while maintaining performance targets. Experiments show up to 20% energy savings while maintaining high-temperature averages and peaks below the threshold. Compared with state-of-the-art temperature predictors, this paper predicts 35% faster and reduces the mean absolute error from 3.25 to 1.15 °C for the evaluated applications' scenarios

Efficient runtime management for enabling sustainable performance in real-world mobile applications

Mobile devices have become integral parts of our society. They handle our diverse computing needs from simple daily tasks (i.e., text messaging, e-mail) to complex graphics and media processing under a limited battery budget. Mobile system-on-chip (SoC) designs have become increasingly sophisticated to handle performance needs of diverse workloads and to improve user experience. Unfortunately, power and thermal constraints have also emerged as major concerns. Increased power densities and temperatures substantially impair user experience due to frequent throttling as well as diminishing device reliability and battery life. Addressing these concerns becomes increasingly challenging due to increased complexities at both hardware (e.g., heterogeneous CPUs, accelerators) and software (e.g., vast number of applications, multi-threading). Enabling sustained user experience in face of these challenges requires (1) practical runtime management solutions that can reason about the performance needs of users and applications while optimizing power and temperature; (2) tools for analyzing real-world mobile application behavior and performance. This thesis aims at improving sustained user experience under thermal limitations by incorporating insights from real-world mobile applications into runtime management. This thesis first proposes thermally-efficient and Quality-of-Service (QoS) aware runtime management techniques to enable sustained performance. Our work leverages inherent QoS tolerance of users in real-world applications and introduces QoS-temperature tradeoff as a viable control knob to improve user experience under thermal constraints. We present a runtime control framework, QScale, which manages CPU power and scheduling decisions to optimize temperature while strictly adhering to given QoS targets. We also design a framework, Maestro, which provides autonomous and application-aware management of QoS-temperature tradeoffs. Maestro uses our thermally-efficient QoS control framework, QScale, as its foundation. This thesis also presents tools to facilitate studies of real-world mobile applications. We design a practical record and replay system, RandR, to generate repeatable executions of mobile applications. RandR provides this capability by automatically reproducing non-deterministic input sources in mobile applications such as user inputs and network events. Finally, we focus on the non-deterministic executions in Android malware which seek to evade analysis environments. We propose the Proteus system to identify the instruction-level inputs that reveal analysis environments

Novel DVFS Methodologies For Power-Efficient Mobile MPSoC

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 thesis 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

QUAREM: Maximising QoE Through Adaptive Resource Management in Mobile MPSoC Platforms

Heterogeneous multi-processor system-on-chip (MPSoC) smartphones are required to offer increasing performance and user quality-of-experience (QoE) , despite comparatively slow advances in battery technology. Approaches to balance instantaneous power consumption, performance and QoE have been reported, but little research has considered how to perform longer-term budgeting of resources across a complete battery discharge cycle. Approaches that have considered this are oblivious to the daily variability in the user’s desired charging time-of-day (plug-in time), resulting in a failure to meet the user’s battery life expectations, or else an unnecessarily over-constrained QoE. This paper proposes QUAREM, an adaptive resource management approach in mobile MPSoC platforms that maximises QoE while meeting battery life expectations. The proposed approach utilises a model that learns and then predicts the dynamics of the energy usage pattern and plug-in times. Unlike state-of-the-art approaches, we maximise the QoE through the adaptive balancing of the battery life and the quality of service (QoS) for the duration of the battery discharge. Our model achieves a good degree of accuracy with a mean absolute percentage error of 3.47% and 2.48% for the energy demand and plug-in times, respectively. Experimental evaluation on an off-the-shelf commercial smartphone shows that QUAREM achieves the expected battery life of the user within 20–25% energy demand variation with little or no QoE degradation. </jats:p