Nowadays, deep convolutional neural networks (DCNNs) play a significant role in many application domains, such as computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, challenging, and time-consuming task, due to the extremely large design space, as a consequence of a large number of layers and their corresponding hyperparameters. In this work, we address the challenge of performing hyperparameter optimization of DCNNs through a novel multiagent reinforcement learning (MARL)-based approach, eliminating the human effort. In particular, we adapt$Q$-learning and define learning agents per layer to split the design space into independent smaller design subspaces such that each agent fine tunes the hyperparameters of the assigned layer concerning a global reward. Moreover, we provide a novel formation of$Q$-tables along with a new update rule that facilitates agents’ communication. Our MARL-based approach is data driven and able to consider an arbitrary set of design objectives and constraints. We apply our MARL-based solution to different well-known DCNNs, including GoogLeNet, VGG, and U-Net, and various datasets for image classification and semantic segmentation. Our results have shown that compared to the original CNNs, the MARL-based approach can reduce the model size, training time, and inference time by up to, respectively,$83\times $, 52%, and 54% without any degradation in accuracy. Moreover, our approach is very competitive to state-of-the-art neural architecture search methods in terms of the designed CNN accuracy and its number of parameters while significantly reducing the optimization cost.

Dynamic Thermal Management (DTM) has become a major challenge since it directly affects Multiprocessors Systems-on-chip (MPSoCs) performance, power consumption, and reliability. In this work, we propose a transient fan model, enabling adaptive fan speed control simulation for efficient DTM. Our model is validated through a thermal test chip achieving less than 2°C error in the worst case. With multiple fan speeds, however, the DTM design space grows significantly, which can ultimately make conventional solutions impractical. We address this challenge through a reinforcement learning-based solution to proactively determine the number of active cores, operating frequency, and fan speed. The proposed solution is able to reduce fan power by up to 40% compared to a DTM with constant fan speed with less than 1% performance degradation. Also, compared to a state-of-the-art DTM technique our solution improves the performance by up to 19% for the same fan power.

The advent of online video streaming applications and services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher video quality and more compression at the cost of increased complexity. On one hand, HEVC exposes a set of dynamically tunable parameters to provide trade-offs among Quality-of-Service (QoS), performance, and power consumption of multi-core servers on the video providers' data center. On the other hand, resource management of modern multi-core servers is in charge of adapting system-level parameters, such as operating frequency and multithreading, to deal with concurrent applications and their requirements. Therefore, efficient multi-user HEVC streaming necessitates joint adaptation of application-and system-level parameters. Nonetheless, dealing with such a large and dynamic design space is challenging and difficult to address through conventional resource management strategies. Thus, in this work, we develop a multi-agent Reinforcement Learning framework to jointly adjust application-and system-level parameters at runtime to satisfy the QoS of multi-user HEVC streaming in power-constrained servers. In particular, the design space, composed of all design parameters, is split into smaller independent sub-spaces. Each design sub-space is assigned to a particular agent so that it can explore it faster, yet accurately. The benefits of our approach are revealed in terms of adaptability and quality (with up to to 4× improvements in terms of QoS when compared to a static resource management scheme), and learning time (6× fasterthan an equivalent mono-agent implementation). Finally, we show that the power-capping techniques formulated outperform the hardware-based power capping with respect to quality.

Real-time video transcoding has recently raised as a valid alternative to address the ever-increasing demand for video contents in servers' infrastructures in current multi-user environments. High Efficiency Video Coding (HEVC) makes efficient online transcoding feasible as it enhances user experience by providing the adequate video configuration, reduces pressure on the network, and minimizes inefficient and costly video storage. However, the computational complexity of HEVC, together with its myriad of configuration parameters, raises challenges for power management, throughput control, and Quality of Service (QoS) satisfaction. This is particularly challenging in multi-user environments where multiple users with different resolution demands and bandwidth constraints need to be served simultaneously. In this work, we present MAMUT, a multi-agent machine learning approach to tackle these challenges. Our proposal breaks the design space composed of run-time adaptation of the transcoder and system parameters into smaller sub-spaces that can be explored in a reasonable time by individual agents. While working cooperatively, each agent is in charge of learning and applying the optimal values for internal HEVC and system-wide parameters. In particular, MAMUT dynamically tunes Quantization Parameter, selects number of threads per video, and sets the operating frequency with throughput and video quality objectives under compression and power consumption constraints. We implement MAMUT on an enterprise multicore server and compare equivalent scenarios to state-of-the-art alternative approaches. The obtained results reveal that MAMUT consistently attains up to 8× improvement in terms of FPS violations (and thus Quality of Service), 24% power reduction, as well as faster and more accurate adaptation both to the video contents and available resources.

The power density and, consequently, power hungriness of server processors is growing by the day. Traditional air cooling systems fail to cope with such high heat densities, whereas single-phase liquid-cooling still requires high mass flow-rate, high pumping power, and large facility size. On the contrary, in a micro-scale gravity-driven thermosyphon attached on top of a processor, the refrigerant, absorbing the heat, turns into a two-phase mixture. The vapor-liquid mixture exchanges heat with a coolant at the condenser side, turns back to liquid state, and descends thanks to gravity, eliminating the need for pumping power. However, similar to other cooling technologies, thermosyphon efficiency can considerably vary with respect to workload performance requirements and thermal profile, in addition to the platform features, such as packaging and die floorplan. In this work, we first address the workload- and platform-aware design of a two-phase thermosyphon. Then, we propose a thermal-aware workload mapping strategy considering the potential and limitations of a two-phase thermosyphon to further minimize hot spots and spatial thermal gradients. Our experiments, performed on an 8-core Intel Xeon E5 CPU reveal, on average, up to 10°C reduction in thermal hot spots, and 45% reduction in the maximum spatial thermal gradient on the die. Moreover, our design and mapping strategy are able to decrease the chiller cooling power at least by 45%.

Accurate workload prediction and throughput estimation are keys in efficient proactive power and performance management of multi-core platforms. Although hardware performance counters available on modern platforms contain important information about the application behavior, employing them efficiently is not straightforward when dealing with time-varying applications even if they have iterative structures. In this work, we propose a machine learning-based framework for workload prediction and throughput estimation using hardware events. Our framework enables throughput estimation over various available system configurations, namely, number of parallel threads and operating frequency. In particular, we first employ workload clustering and classification techniques along with Markov chains to predict the next workload for each available system configuration. Then, the predicted workload is used to estimate the next expected throughput through a machine learning-based regression model. The comparison with state of the art demonstrates that our framework is able to improve Quality of Service (QoS) by 3.4x, while consuming 15% less power thanks to the more accurate throughput estimation.

In this work we present ContainEnergy, a new performance evaluation and profiling tool that uses software containers to perform application runtime assessment, providing energy and performance profiling data. It is focused on energy efficiency for next generation workloads and IT infrastructure.

Bio-medical image processing in the field of telemedicine, and in particular the definition of systems that allow medical diagnostics in a collaborative and distributed way is experiencing an undeniable growth. Due to the high quality of bio-medical videos and the subsequent large volumes of data generated, to enable medical diagnosis on-the-go it is imperative to efficiently transcode and stream the stored videos on real time, without quality loss. However, online video transcoding is a high-demanding computationally-intensive task and its efficient management in Multiprocessor Systems-on-Chip (MPSoCs) poses an important challenge. In this work, we propose an efficient motion- and texture-aware frame-level parallelization approach to enable online medical imaging transcoding on MPSoCs for next generation video encoders. By exploiting the unique characteristics of bio-medical videos and the medical procedure that enable diagnosis, we split frames into tiles based on their motion and texture, deciding the most adequate level of parallelization. Then, we employ the available encoding parameters to satisfy the required video quality and compression. Moreover, we propose a new fast motion search algorithm for bio-medical videos that allows to drastically reduce the computational complexity of the encoder, thus achieving the frame rates required for online transcoding. Finally, we heuristically allocate the threads to the most appropriate available resources and set the operating frequency of each one. We evaluate our work on an enterprise multicore server achieving online medical imaging with 1.6x higher throughput and 44% less power consumption when compared to the state-of-the-art techniques.

The design of new streaming systems is becoming a major area of research to deploy services targeted in the Internet-of-Things (IoT) era. In this context, the new High Efficiency Video Coding (HEVC) standard provides high efficiency and scalability of quality at the cost of increased computational complexity for edge nodes, which is a new challenge for the design of IoT systems. The usage of hardware acceleration in conjunction with general-purpose cores in Multiprocessor Systems-on-Chip (MP-SoCs) is a promising solution to create heterogeneous computing systems to manage the complexity of real-time streaming for high-end IoT systems, achieving higher throughput and power efficiency when compared to conventional processors alone. Furthermore, Machine Learning (ML) provides a promising solution to efficiently use this next-generation of heterogeneous MPSoC designs that the EDA industry is developing by dynamically optimizing system performance under diverse requirements such as frame resolution, search area, operating frequency and stream allocation. In this work, we propose an ML-based approach for stream allocation and Dynamic Voltage and Frequency Scaling (DVFS) management on a heterogeneous MPSoC composed of ARM cores and FPGA fabric containing hardware accelerators for the motion estimation of HEVC encoding. Our experiments on a Zynq7000 SoC outline 20% higher throughput when compared to the state-of-the-art streaming systems for next-generation IoT devices.

Thermal stress including temperature gradients in time and space, as well as thermal cycling, influences lifetime reliability and performance of modern multiprocessor systems-on-chip (MPSoCs). Conventional power and temperature management techniques considering the peak temperature/power consumption do not provide a comprehensive solution to avoid high spatial and temporal thermal variations. This work presents TheSPoT, a novel multilevel thermal stress-aware power and thermal management approach for MPSoCs. At the top level, core consolidation and deconsolidation is performed based on peak temperature, thermal stress, and power consumption constraints. These constraints are also used at the next level, where operating frequencies are determined. At this level, we obtain optimal core frequencies by solving a convex optimization problem. However, thereafter, to reduce the runtime overhead in large MPSoCs, we alternatively propose to use a fast heuristic algorithm. The efficacy of the proposed approaches in reducing the thermal cycles and temporal/spatial temperature gradients is evaluated by comparing the results with the state-of-the-art methods. The evaluation performed on 4-core, 8-core, and 16-core MPSoCs, using PARSEC benchmarks, reveals a considerable reduction in thermal stress. For the 8-core MPSoC case study, on average, for the proposed heuristic(optimal) approach, the mean time to failure improved by 47(35)% compared to the state-of-the-art techniques with only 6(4)% performance degradation. Also, our simulations show that TheSPoT is more efficient in thermal stress reduction when more heterogeneous workloads are used.

The emergence of video streaming applications, together with the users' demand for high-resolution contents, has led to the development of new video coding standards, such as High Efficiency Video Coding (HEVC). HEVC provides high efficiency at the cost of increased complexity. This higher computational burden results in increased power consumption in current multicore servers. To tackle this challenge, algorithmic optimizations need to be accompanied by content-aware application-level strategies, able to reduce power while meeting compression and quality requirements. In this paper, we propose a machine learning-based power and thermal management approach that dynamically learns and selects the best encoding configuration and operating frequency for each of the videos running on multicore servers, by using information from frame compression, quality, encoding time, power, and temperature. In addition, we present a resolution-aware video assignment and migration strategy that reduces the peak and average temperature of the chip while maintaining the desirable encoding time. We implemented our approach in an enterprise multicore server and evaluated it under several common scenarios for video providers. On average, compared to a state-of-the-art technique, for the most realistic scenario, our approach improves BD-PSNR and BD-rate by 0.54 dB, and 8 percent, respectively, and reduces the encoding time, power consumption, and average temperature by 15.3, 13, and 10 percent, respectively. Moreover, our proposed approach enhances BDPSNR and BD-rate compared to the HEVC Test Model (HM), by 1.19 dB and 24 percent, respectively, without any encoding time degradation, when power and temperature constraints are relaxed.

The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated.

In this work, we propose a power and thermal management algorithm based on machine learning to control the thermal stresses and power consumption of the heterogeneous MPSoCs. The objectives of the proposed algorithm are increasing the performance and decreasing the spatial and temporal temperature gradients along with the thermal cycling under the power and temperature constraints. Our proposed power and thermal management method is based on a heuristic approach to speed up the convergence of the machine learning algorithm which makes it applicable for general purpose processors. Adopting Q-Learning as the machine learning algorithm, the heuristic approach aids to limit the learning space by suggesting the most appropriate actions to the agent in each decision epoch. The heuristic algorithm employs the current and previous states of the machine learning, as well as the amount of the temperature stress and power consumption of each core to determine the appropriate action for each core, independently. The proposed algorithm is evaluated on 4-core, 8-core and 16-core homogeneous and heterogeneous MPSoCs for some benchmarks in the Splash2 benchmark package. The results reveal a faster convergence of machine learning and more thermal stresses reduction.