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Mini data centres have become increasingly prevalent in diverse organizations in recent years. They can be easily deployed at large scale, with high resilience. They are also cost-effective and provide highsecurity protection. On the other hand, IT technologies have resulted in the development of ever more energy-efficient servers, leading to the periodic replacement of older-generation servers in mini data centres. However, the disposal of older servers has resulted in electronic waste that further aggravates the already critical e-waste problem. Furthermore, despite the shift towards more energy-efficient servers, many mini data centres still rely heavily on high-carbon energy sources. This contributes to data centres' overall carbon footprint. All these issues are concerns for sustainability. In order to address this sustainability issue, this paper proposes an approach to extend the lifespan of older-generation servers in mini data centres. This is made possible thanks to a novel solar-powered computing technology, named Genesis, that compensates for the energy overhead generated by older servers. As a result, electronic waste can be reduced while improving system sustainability by reusing functional server hardware. Moreover, Genesis does not require server cooling, which reduces energy and water requirements. Analytical reasoning is applied to compare the efficiency of typical conventional mini data centre designs against alternative Genesis-based designs, in terms of energy, carbon emissions and exploitation costs.

This paper investigates the application of eXplainable Artificial Intelligence (XAI) in the design of embedded systems using machine learning (ML). As a case study, it addresses the challenging problem of static silent store prediction. This involves identifying redundant memory writes based only on static program features. Eliminating such stores enhances performance and energy efficiency by reducing memory access and bus traffic, especially in the presence of emerging non-volatile memory technologies. To achieve this, we propose a methodology consisting of: 1) the development of relevant ML models for explaining silent store prediction, and 2) the application of XAI to explain these models. We employ two state-of-the-art model-agnostic XAI methods to analyze the causes of silent stores. Through the case study, we evaluate the effectiveness of the methods. We find that these methods provide explanations for silent store predictions, which are consistent with known causes of silent store occurrences from previous studies. Typically, this allows us to confirm the prevalence of silent stores in operations that write the zero constant into memory, or the absence of silent stores in operations involving loop induction variables. This suggests the potential relevance of XAI in analyzing ML models' decision in embedded system design. From the case study, we share some valuable insights and pitfalls we encountered. More generally, this study aims to lay the groundwork for future research in the emerging field of XAI for embedded system design.

Energy-aware architectures provide applications with a mix of low (LITTLE) and high (big) frequency cores. Choosing the best hardware configuration for a program running on such an architecture is difficult, because program parts benefit differently from the same hardware configuration. State-of-the-art techniques to solve this problem adapt the program's execution to dynamic characteristics of the runtime environment, such as energy consumption and throughput. We claim that these purely dynamic techniques can be improved if they are aware of the program's syntactic structure. To support this claim, we show how to use the compiler to partition source code into program phases: regions whose syntactic characteristics lead to similar runtime behavior. We use reinforcement learning to map pairs formed by a program phase and a hardware state to the configuration that best fit this setup. To demonstrate the effectiveness of our ideas, we have implemented the Astro system. Astro uses Q-learning to associate syntactic features of programs with hardware configurations. As a proof of concept, we provide evidence that Astro outperforms GTS, the ARM-based Linux scheduler tailored for heterogeneous architectures, on the parallel benchmarks from Rodinia and Parsec.

Energy-efficiency has become a major challenge in modern computer systems. To address this challenge, candidate systems increasingly integrate heterogeneous cores in order to satisfy diverse computation requirements by selecting cores with suitable features. In particular, single-ISA heterogeneous multicore processors such as ARM big.LITTLE have become very attractive since they offer good opportunities in terms of performance and power consumption trade-off. While existing works already showed that this feature can improve system energy-efficiency, further gains are possible by generalizing the principle to higher levels of heterogeneity. The present paper aims to explore these gains by considering single-ISA heterogeneous multicore architectures including three different types of cores. For this purpose, we use the Samsung Exynos Octa 5422 chip as baseline architecture. Then, we model and evaluate Cortex A7, A9, and A15 cores using the gem5 simulation framework coupled to McPAT for power estimation. We demonstrate that varying the level of heterogeneity as well as the different core ratio can lead to up to 2.3x gains in energy efficiency and up to 1.5x in performance. This study further provides insights on the impact of workload nature on performance/energy trade-off and draws recommendations concerning suitable architecture configurations. This contributes in fine to guide future research towards dynamically reconfigurable HSAs in which some cores/clusters can be disabled momentarily so as to optimize certain metrics such as energy efficiency. This is of particular interest when dealing with quality-tunable algorithms in which accuracy can be then traded for compute effort, thereby enabling to use only those cores that provide the best energy-efficiency for the chosen algorithm., Comment: 11 pages, 6 figure, 2 tables

Resource allocation is today an integral part of cloud infrastructures management to efficiently exploit resources. Cloud infrastructures centers generally use custom built heuristics to define the resource allocations. It is an immediate requirement for the management tools of these centers to have a fast yet reasonably accurate simulation and evaluation platform to define the resource allocation for cloud applications. This work proposes a framework allowing users to easily specify mappings for cloud applications described in the AMALTHEA format used in the context of the DreamCloud European project and to assess the quality for these mappings. The two quality metrics provided by the framework are execution time and energy consumption., Comment: 2nd International Workshop on Dynamic Resource Allocation and Management in Embedded, High Performance and Cloud Computing DREAMCloud 2016 (arXiv:cs/1601.04675)