A New Power Analysis Attack and a Countermeasure in Embedded Systems

Energy saving is extremely important in portable and hand-held devices like laptop and mobile phones. As applications become increasingly sophisticated and processing power increases, the most serious limitation on these devices is the available battery life. One promising technique that provides the best of both low-power and high-performance processors in the real time systems is Dynamic Voltage and Frequency Scaling (DVS). In this paper, we are working on the problem of power optimization for periodic independent real-time tasks that are scheduled over multiprocessors. Theoretically, Real Time tasks are specified with Worst Case Execution Time (WCET). During the operation of the system, the Actual Execution Time (AET) of a task could be a lot smaller than its WCET. We present an Adaptive real time DVS algorithm (Art-DVS) based on AET of tasks. It adapts two state-of-the-art scheduling algorithms Pre-DVS and Post-DVS. Simulation results show that the proposed Art-DVS algorithm performs satisfactorily on energy consumption in comparison to the previous approaches.

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.

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.

In quantum key distribution (QKD), there are some security loopholes opened by the gaps between the theoretical model and the practical system, and they may be exploited by eavesdroppers (Eve) to obtain secret key information without being detected. This is an effective quantum hacking strategy that seriously threatens the security of practical QKD systems. In this paper, we propose a new quantum hacking attack on an integrated silicon photonic continuous-variable quantum key distribution (CVQKD) system, which is known as a power analysis attack. This attack can be implemented by analyzing the power originating from the integrated electrical control circuit in state preparation with the help of machine learning, where the state preparation is assumed to be perfect in initial security proofs. Specifically, we describe a possible power model and show a complete attack based on a support vector regression (SVR) algorithm. The simulation results show that the secret key information decreases with the increase of the accuracy of the attack, especially in a situation with less excess noise. In particular, Eve does not have to intrude into the transmitter chip (Alice), and may perform a similar attack in practical chip-based discrete-variable quantum key distribution (DVQKD) systems. To resist this attack, the electrical control circuit should be improved to randomize the corresponding power. In addition, the power can be reduced by utilizing the dynamic voltage and frequency scaling (DVFS) technology.

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) is one of the most commonly used power reduction techniques in high-performance processors. DVFS varies the frequency and voltage of a microprocessor in real-time according to processing needs. Although there are different versions of DVFS, at its core DVFS adapts power consumption and performance to the current workload of the CPU. Specifically, existing DVFS techniques in high-performance processors select an operating point (CPU frequency and voltage) based on the utilization of the processor. This approach integrates OS-level control, but such control is pessimistic. Existing DVFS techniques are pessimistic about the user. Indeed, they ignore the user, assuming that CPU utilization or the OS events prompting it are sufficient proxies. A high CPU utilization simply leads to a high frequency and high voltage, regardless of the user’s satisfaction or expectation of performance. Existing DVFS techniques are pessimistic about the CPU. They assume worst-case manufacturing process variation and operating temperature by basing their policies on loose worstcase bounds given by the processor manufacturer. A voltage level for a given frequency is set such that even the worst shipped processor of a given generation will be stable at the highest specified temperature. In response to these observations, we have developed, implemented, and evaluated the following two new power management techniques that can be readily employed independently or together. We elaborate on these techniques in detail elsewhere [4].

Dynamic Voltage and Frequency Scaling (DVFS) is an essential part of controlling the power consumption of any computer system, ranging from mobile phones to servers. DVFS efficiency relies on hardware-software co-optimization, thus using existing hardware cannot reveal the full optimization potential beyond the current implementation's characteristics. To explore the vast design space for DVFS efficiency, that straddles software and hardware, a simulation infrastructure must provide features that are not readily available today, for example: software controllable clock and voltage domains, support for the OS and the frequency scaling module of it, and an online power estimation methodology. As the main contribution, this work enables DVFS studies in a full-system simulator. We extend the gem5 simulator to support full-system DVFS modeling. By doing so, we enable energy-efficiency experiments to be performed in gem5 and we showcase such studies. Finally, we show that both existing and novel frequency governors for Linux and Android can be effortlessly integrated in the framework, and we evaluate the efficiency of different DVFS schemes.

Many power management strategies have been proposed for enterprise servers based on dynamic voltage and frequency scaling (DVFS), but those solutions cannot further reduce the energy consumption of a server when the server processor is already at the lowest DVFS level and the server utilization is still low (e.g., 5% or lower). To achieve improved energy efficiency, request batching can be conducted to group received requests into batches and put the processor into sleep between the batches. However, it is challenging to perform request batching on a virtualized server because different virtual machines on the same server may have different workload intensities. Hence, putting the shared processor into sleep may severely impact the performance of all the virtual machines. This paper proposes Virtual Batching, a novel request batching solution for virtualized servers with primarily light workloads. Our solution dynamically allocates CPU resources such that all the virtual machines can have approximately the same performance level relative to their allowed peak values. Based on this uniform level, our solution determines the time length for periodically batching incoming requests and putting the processor into sleep. When the workload intensity changes from light to moderate, request batching is automatically switched to DVFS to increase processor frequency for performance guarantees. Empirical results based on a hardware testbed and real trace files show that Virtual Batching can achieve the desired performance with more energy conservation than several well-designed baselines, e.g., 63% more, on average, than a solution based on DVFS only.

For energy efficient scheduling of task graphs on multiprocessors, dynamic voltage and frequency scaling (DVFS) and duplication are two widely used techniques. DVFS is generally used to utilize the execution slack by lowering the voltage and frequency of a task to decrease the dynamic energy consumption. Whereas duplication decreases the schedule length and communication energy consumption by replicating certain dependent tasks to avoid communication delays. However, while making decisions on DVFS and duplication for a task, the static energy consumption is mostly overlooked. With chip technologies reducing to a few nano meters, static energy consumption due to leakage current has become important. This article proposes a novel polynomial time heuristic that uses both DVFS and duplication to optimize static energy consumption along with dynamic and communication energy when scheduling task graphs on heterogeneous multiprocessors. The proposed list scheduling algorithm also balances schedule length with energy consumption using proposed normalized difference parameters while making scheduling decisions for a particular task. The results demonstrate the ability of the proposed algorithm to decrease the overall energy consumption with an improved or comparable schedule length as compared with other algorithms in various scenarios.

Dynamic Voltage and Frequency Scaling (DVFS) has been widely deployed and proven to reduce energy consumption at low CPU utilization levels; however, our measurements of the n-tier application benchmark (RUBBoS) performance showed significant performance degradation at high utilization levels, with response time several times higher and throughput loss of up to 20%, when DVFS is turned on. Using a combination of benchmark measurements and simulation, we found two kinds of problems: large response time fluctuations due to push-back wave queuing in n-tier systems and throughput loss due to rapidly alternating bottlenecks. These problems arise from anti-synchrony between DVFS adjustment period and workload burst cycles (similar cycle length but out of phase). Simulation results (confirmed by extensive measurements) show the anti-synchrony happens routinely for a wide range of configurations. We show that a workload-sensitive DVFS adaptive control mechanism can disrupt the anti-synchrony and reduce the performance impact of DVFS at high utilization levels to 25% or less of the original.

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.

In designing information communication devices such as real-time embedded systems, it is quite important to maximize the system performance under some hard energy constraints. As a useful technology to reduce the energy consumption in computer-based systems, the dynamic voltage and frequency scaling (DVFS) is becoming very popular. In this paper, we consider an optimal DVFS allocation problem by maximizing the system reliability subject to the hard real-time and energy constraints. More specifically, we formulate a reliability maximization problem when the task processing is probabilistic and is described by a discrete-time Markov chain. Two approximate formulas are proposed to calculate the system reliability efficiently. We perform the sensitivity analysis of model parameters in numerical examples, and also give a case study to design a Wi-Fi subsystem in terms of the DVFS allocation.

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.