Tecniche per il controllo dinamico del consumo di potenza per piattaforme system-on-chip

Providing support for multimedia applications on low-power mobile devices remains a significant research challenge. This is primarily due to two reasons: • Portable mobile devices have modest sizes and weights, and therefore inadequate resources, low CPU processing power, reduced display capabilities, limited memory and battery lifetimes as compared to desktop and laptop systems. • On the other hand, multimedia applications tend to have distinctive QoS and processing requirementswhichmake themextremely resource-demanding. This innate conflict introduces key research challenges in the design of multimedia applications and device-level power optimization. Energy efficiency in this kind of platforms can be achieved only via a synergistic hardware and software approach. In fact, while System-on-Chips are more and more programmable thus providing functional flexibility, hardwareonly power reduction techniques cannot maintain consumption under acceptable bounds. It is well understood both in research and industry that system configuration andmanagement cannot be controlled efficiently only relying on low-level firmware and hardware drivers. In fact, at this level there is lack of information about user application activity and consequently about the impact of power management decision on QoS. Even though operating system support and integration is a requirement for effective performance and energy management, more effective and QoSsensitive power management is possible if power awareness and hardware configuration control strategies are tightly integratedwith domain-specificmiddleware services. The main objective of this PhD research has been the exploration and the integration of amiddleware-centric energymanagement with applications and operating-system. We choose to focus on the CPU-memory and the video subsystems, since they are the most power-hungry components of an embedded system. A second main objective has been the definition and implementation of software facilities (like toolkits, API, and run-time engines) in order to improve programmability and performance efficiency of such platforms. Enhancing energy efficiency and programmability ofmodernMulti-Processor System-on-Chips (MPSoCs) Consumer applications are characterized by tight time-to-market constraints and extreme cost sensitivity. The software that runs on modern embedded systems must be high performance, real time, and even more important low power. Although much progress has been made on these problems, much remains to be done. Multi-processor System-on-Chip (MPSoC) are increasingly popular platforms for high performance embedded applications. This leads to interesting challenges in software development since efficient software development is a major issue for MPSoc designers. An important step in deploying applications on multiprocessors is to allocate and schedule concurrent tasks to the processing and communication resources of the platform. The problem of allocating and scheduling precedenceconstrained tasks on processors in a distributed real-time system is NP-hard. There is a clear need for deployment technology that addresses thesemulti processing issues. This problem can be tackled by means of specific middleware which takes care of allocating and scheduling tasks on the different processing elements and which tries also to optimize the power consumption of the entire multiprocessor platform. This dissertation is an attempt to develop insight into efficient, flexible and optimalmethods for allocating and scheduling concurrent applications tomultiprocessor architectures. It is a well-known problem in literature: this kind of optimization problems are very complex even in much simplified variants, therefore most authors propose simplified models and heuristic approaches to solve it in reasonable time. Model simplification is often achieved by abstracting away platform implementation ”details”. As a result, optimization problems become more tractable, even reaching polynomial time complexity. Unfortunately, this approach creates an abstraction gap between the optimization model and the real HW-SW platform. The main issue with heuristic or, more in general, with incomplete search is that they introduce an optimality gap of unknown size. They provide very limited or no information on the distance between the best computed solution and the optimal one. The goal of this work is to address both abstraction and optimality gaps, formulating accurate models which accounts for a number of ”non-idealities” in real-life hardware platforms, developing novel mapping algorithms that deterministically find optimal solutions, and implementing software infrastructures required by developers to deploy applications for the targetMPSoC platforms. Energy Efficient LCDBacklightAutoregulation on Real-LifeMultimediaAp- plication Processor Despite the ever increasing advances in Liquid Crystal Display’s (LCD) technology, their power consumption is still one of the major limitations to the battery life of mobile appliances such as smart phones, portable media players, gaming and navigation devices. There is a clear trend towards the increase of LCD size to exploit the multimedia capabilities of portable devices that can receive and render high definition video and pictures. Multimedia applications running on these devices require LCD screen sizes of 2.2 to 3.5 inches andmore to display video sequences and pictures with the required quality. LCD power consumption is dependent on the backlight and pixel matrix driving circuits and is typically proportional to the panel area. As a result, the contribution is also likely to be considerable in future mobile appliances. To address this issue, companies are proposing low power technologies suitable for mobile applications supporting low power states and image control techniques. On the research side, several power saving schemes and algorithms can be found in literature. Some of them exploit software-only techniques to change the image content to reduce the power associated with the crystal polarization, some others are aimed at decreasing the backlight level while compensating the luminance reduction by compensating the user perceived quality degradation using pixel-by-pixel image processing algorithms. The major limitation of these techniques is that they rely on the CPU to perform pixel-based manipulations and their impact on CPU utilization and power consumption has not been assessed. This PhDdissertation shows an alternative approach that exploits in a smart and efficient way the hardware image processing unit almost integrated in every current multimedia application processors to implement a hardware assisted image compensation that allows dynamic scaling of the backlight with a negligible impact on QoS. The proposed approach overcomes CPU-intensive techniques by saving system power without requiring either a dedicated display technology or hardware modification. Thesis Overview The remainder of the thesis is organized as follows. The first part is focused on enhancing energy efficiency and programmability of modern Multi-Processor System-on-Chips (MPSoCs). Chapter 2 gives an overview about architectural trends in embedded systems, illustrating the principal features of new technologies and the key challenges still open. Chapter 3 presents a QoS-driven methodology for optimal allocation and frequency selection for MPSoCs. The methodology is based on functional simulation and full system power estimation. Chapter 4 targets allocation and scheduling of pipelined stream-oriented applications on top of distributed memory architectures with messaging support. We tackled the complexity of the problem by means of decomposition and no-good generation, and prove the increased computational efficiency of this approach with respect to traditional ones. Chapter 5 presents a cooperative framework to solve the allocation, scheduling and voltage/frequency selection problem to optimality for energyefficient MPSoCs, while in Chapter 6 applications with conditional task graph are taken into account. Finally Chapter 7 proposes a complete framework, called Cellflow, to help programmers in efficient software implementation on a real architecture, the Cell Broadband Engine processor. The second part is focused on energy efficient software techniques for LCD displays. Chapter 8 gives an overview about portable device display technologies, illustrating the principal features of LCD video systems and the key challenges still open. Chapter 9 shows several energy efficient software techniques present in literature, while Chapter 10 illustrates in details our method for saving significant power in an LCD panel. Finally, conclusions are drawn, reporting the main research contributions that have been discussed throughout this dissertation.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.

Effects of environmental complexity on neurogenesis in the hippocampal dentate gyrus

Introduction. Postnatal neurogenesis in the hippocampal dentate gyrus, can be modulated by numerous determinants, such as hormones, transmitters and stress. Among the factors positively interfering with neurogenesis, the complexity of the environment appears to play a particularly striking role. Adult mice reared in an enriched environment produce more neurons and exhibit better performance in hippocampus-specific learning tasks. While the effects of complex environments on hippocampal neurogenesis are well documented, there is a lack of information on the effects of living under socio-sensory deprivation conditions. Due to the immaturity of rats and mice at birth, studies dealing with the effects of environmental enrichment on hippocampal neurogenesis were carried out in adult animals, i.e. during a period of relatively low rate of neurogenesis. The impact of environment is likely to be more dramatic during the first postnatal weeks, because at this time granule cell production is remarkably higher than at later phases of development. The aim of the present research was to clarify whether and to what extent isolated or enriched rearing conditions affect hippocampal neurogenesis during the early postnatal period, a time window characterized by a high rate of precursor proliferation and to elucidate the mechanisms underlying these effects. The experimental model chosen for this research was the guinea pig, a precocious rodent, which, at 4-5 days of age can be independent from maternal care. Experimental design. Animals were assigned to a standard (control), an isolated, or an enriched environment a few days after birth (P5-P6). On P14-P17 animals received one daily bromodeoxyuridine (BrdU) injection, to label dividing cells, and were sacrificed either on P18, to evaluate cell proliferation or on P45, to evaluate cell survival and differentiation. Methods. Brain sections were processed for BrdU immunhistochemistry, to quantify the new born and surviving cells. The phenotype of the surviving cells was examined by means of confocal microscopy and immunofluorescent double-labeling for BrdU and either a marker of neurons (NeuN) or a marker of astrocytes (GFAP). Apoptotic cell death was examined with the TUNEL method. Serial sections were processed for immunohistochemistry for i) vimentin, a marker of radial glial cells, ii) BDNF (brain-derived neurotrofic factor), a neurotrophin involved in neuron proliferation/survival, iii) PSA-NCAM (the polysialylated form of the neural cell adhesion molecule), a molecule associated with neuronal migration. Total granule cell number in the dentate gyrus was evaluated by stereological methods, in Nissl-stained sections. Results. Effects of isolation. In P18 isolated animals we found a reduced cell proliferation (-35%) compared to controls and a lower expression of BDNF. Though in absolute terms P45 isolated animals had less surviving cells than controls, they showed no differences in survival rate and phenotype percent distribution compared to controls. Evaluation of the absolute number of surviving cells of each phenotype showed that isolated animals had a reduced number of cells with neuronal phenotype than controls. Looking at the location of the new neurons, we found that while in control animals 76% of them had migrated to the granule cell layer, in isolated animals only 55% of the new neurons had reached this layer. Examination of radial glia cells of P18 and P45 animals by vimentin immunohistochemistry showed that in isolated animals radial glia cells were reduced in density and had less and shorter processes. Granule cell count revealed that isolated animals had less granule cells than controls (-32% at P18 and -42% at P45). Effects of enrichment. In P18 enriched animals there was an increase in cell proliferation (+26%) compared to controls and a higher expression of BDNF. Though in both groups there was a decline in the number of BrdU-positive cells by P45, enriched animals had more surviving cells (+63) and a higher survival rate than controls. No differences were found between control and enriched animals in phenotype percent distribution. Evaluation of the absolute number of cells of each phenotype showed that enriched animals had a larger number of cells of each phenotype than controls. Looking at the location of cells of each phenotype we found that enriched animals had more new neurons in the granule cell layer and more astrocytes and cells with undetermined phenotype in the hilus. Enriched animals had a higher expression of PSA-NCAM in the granule cell layer and hilus Vimentin immunohistochemistry showed that in enriched animals radial glia cells were more numerous and had more processes.. Granule cell count revealed that enriched animals had more granule cells than controls (+37% at P18 and +31% at P45). Discussion. Results show that isolation rearing reduces hippocampal cell proliferation but does not affect cell survival, while enriched rearing increases both cell proliferation and cell survival. Changes in the expression of BDNF are likely to contribute to he effects of environment on precursor cell proliferation. The reduction and increase in final number of granule neurons in isolated and enriched animals, respectively, are attributable to the effects of environment on cell proliferation and survival and not to changes in the differentiation program. As radial glia cells play a pivotal role in neuron guidance to the granule cell layer, the reduced number of radial glia cells in isolated animals and the increased number in enriched animals suggests that the size of radial glia population may change dynamically, in order to match changes in neuron production. The high PSA-NCAM expression in enriched animals may concur to favor the survival of the new neurons by facilitating their migration to the granule cell layer. Conclusions. By using a precocious rodent we could demonstrate that isolated/enriched rearing conditions, at a time window during which intense granule cell proliferation takes place, lead to a notable decrease/increase of total granule cell number. The time-course and magnitude of postnatal granule cell production in guinea pigs are more similar to the human and non-human primate condition than in rats and mice. Translation of current data to humans would imply that exposure of children to environments poor/rich of stimuli may have a notably large impact on dentate neurogenesis and, very likely, on hippocampus dependent memory functions.

Cache-aware development of high integrity real-time systems

Cost, performance and availability considerations are forcing even the most conservative high-integrity embedded real-time systems industry to migrate from simple hardware processors to ones equipped with caches and other acceleration features. This migration disrupts the practices and solutions that industry had developed and consolidated over the years to perform timing analysis. Industry that are confident with the efficiency/effectiveness of their verification and validation processes for old-generation processors, do not have sufficient insight on the effects of the migration to cache-equipped processors. Caches are perceived as an additional source of complexity, which has potential for shattering the guarantees of cost- and schedule-constrained qualification of their systems. The current industrial approach to timing analysis is ill-equipped to cope with the variability incurred by caches. Conversely, the application of advanced WCET analysis techniques on real-world industrial software, developed without analysability in mind, is hardly feasible. We propose a development approach aimed at minimising the cache jitters, as well as at enabling the application of advanced WCET analysis techniques to industrial systems. Our approach builds on:(i) identification of those software constructs that may impede or complicate timing analysis in industrial-scale systems; (ii) elaboration of practical means, under the model-driven engineering (MDE) paradigm, to enforce the automated generation of software that is analyzable by construction; (iii) implementation of a layout optimisation method to remove cache jitters stemming from the software layout in memory, with the intent of facilitating incremental software development, which is of high strategic interest to industry. The integration of those constituents in a structured approach to timing analysis achieves two interesting properties: the resulting software is analysable from the earliest releases onwards - as opposed to becoming so only when the system is final - and more easily amenable to advanced timing analysis by construction, regardless of the system scale and complexity.

Implicit computational complexity and probabilistic classes

The thesis applies the ICC tecniques to the probabilistic polinomial complexity classes in order to get an implicit characterization of them. The main contribution lays on the implicit characterization of PP (which stands for Probabilistic Polynomial Time) class, showing a syntactical characterisation of PP and a static complexity analyser able to recognise if an imperative program computes in Probabilistic Polynomial Time. The thesis is divided in two parts. The first part focuses on solving the problem by creating a prototype of functional language (a probabilistic variation of lambda calculus with bounded recursion) that is sound and complete respect to Probabilistic Prolynomial Time. The second part, instead, reverses the problem and develops a feasible way to verify if a program, written with a prototype of imperative programming language, is running in Probabilistic polynomial time or not. This thesis would characterise itself as one of the first step for Implicit Computational Complexity over probabilistic classes. There are still open hard problem to investigate and try to solve. There are a lot of theoretical aspects strongly connected with these topics and I expect that in the future there will be wide attention to ICC and probabilistic classes.

Probabilistic Recursion Theory and Implicit Computational Complexity

In this thesis we provide a characterization of probabilistic computation in itself, from a recursion-theoretical perspective, without reducing it to deterministic computation. More specifically, we show that probabilistic computable functions, i.e., those functions which are computed by Probabilistic Turing Machines (PTM), can be characterized by a natural generalization of Kleene's partial recursive functions which includes, among initial functions, one that returns identity or successor with probability 1/2. We then prove the equi-expressivity of the obtained algebra and the class of functions computed by PTMs. In the the second part of the thesis we investigate the relations existing between our recursion-theoretical framework and sub-recursive classes, in the spirit of Implicit Computational Complexity. More precisely, endowing predicative recurrence with a random base function is proved to lead to a characterization of polynomial-time computable probabilistic functions.