Cuestiones actuales del Derecho local alemán

El presente trabajo expone las principales reformas y debates que se han producido en el ámbito del Derecho local de la República Federal de Alemania en los últimos años. Con esa finalidad se analizan los fundamentos básicos constitucionales y legales del Derecho local, así como sus reformas más importantes, previendo los efectos que se han producido o que pueden llegar a producirse.This work deals with the main reforms and debates that have emerged in the field of local government in the last years. With this aim the authors analyze the constitutional and legal foundations of municipal Law in Germany, as well as the most important reforms carried out recently. On that basis they attempt to identify and anticipate the effects that these reforms could have on the German system of local government

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Digital design protection in Europe : law, trends, and emerging issues

Digital designs – that is, designs for display on electronic screens – have recently burst onto the intellectual property (IP) stage. While in the U.S. a smattering of legal studies have recently addressed the question of digital design as a copyright -, rademark - and patent - eligible subject matter, a European perspective is still lacking in the literature. This study provides an overview of basic legal background to the protection of digital designs in Europe, explores firms’ actual digital design protection behaviors, and highlights some important practical and doctrinal issues that warrant further study

Design automation of approximate circuits with runtime reconfigurable accuracy

Leveraging the inherent error tolerance of a vast number of application domains that are rapidly growing, approximate computing arises as a design alternative to improve the efficiency of our computing systems by trading accuracy for energy savings. However, the requirement for computational accuracy is not fixed. Controlling the applied level of approximation dynamically at runtime is a key to effectively optimize energy, while still containing and bounding the induced errors at runtime. In this paper, we propose and implement an automatic and circuit independent design framework that generates approximate circuits with dynamically reconfigurable accuracy at runtime. The generated circuits feature varying accuracy levels, supporting also accurate execution. Extensive experimental evaluation, using industry strength flow and circuits, demonstrates that our generated approximate circuits improve the energy by up to 41% for 2% error bound and by 17.5% on average under a pessimistic scenario that assumes full accuracy requirement in the 33% of the runtime. To demonstrate further the efficiency of our framework, we considered two state-of-the-art technology libraries which are a 7nm conventional FinFET and an emerging technology that boosts performance at a high cost of increased dynamic power

Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices

Federated learning (FL) is usually performed on resource-constrained edge devices, e.g., with limited memory for the computation. If the required memory to train a model exceeds this limit, the device will be excluded from the training. This can lead to a lower accuracy as valuable data and computation resources are excluded from training, also causing bias and unfairness. The FL training process should be adjusted to such constraints. The state-of-the-art techniques propose training subsets of the FL model at constrained devices, reducing their resource requirements for training. But these techniques largely limit the co-adaptation among parameters of the model and are highly inefficient, as we show: it is actually better to train a smaller (less accurate) model by the system where all the devices can train the model end-to-end, than applying such techniques. We propose a new method that enables successive freezing and training of the parameters of the FL model at devices, reducing the training's resource requirements at the devices, while still allowing enough co-adaptation between parameters. We show through extensive experimental evaluation that our technique greatly improves the accuracy of the trained model (by 52.4 p.p.) compared with the state of the art, efficiently aggregating the computation capacity available on distributed devices.Comment: accepted at NeurIPS'2

Energy Optimization in NCFET-based Processors

Energy consumption is a key optimization goal for all modern processors. Negative Capacitance Field-Effect Transistors (NCFETs) are a leading emerging technology that promises outstanding performance in addition to better energy efficiency. Thickness of the additional ferroelectric layer, frequency, and voltage are the key parameters in NCFET technology that impact the power and frequency of processors. However, their joint impact on energy optimization has not been investigated yet.In this work, we are the first to demonstrate that conventional (i.e., NCFET-unaware) dynamic voltage/frequency scaling (DVFS) techniques to minimize energy are sub-optimal when applied to NCFET-based processors. We further demonstrate that state-of-the-art NCFET-aware voltage scaling for power minimization is also sub-optimal when it comes to energy. This work provides the first NCFET-aware DVFS technique that optimizes the processor\u27s energy through optimal runtime frequency/voltage selection. In NCFETs, energy-optimal frequency and voltage are dependent on the workload and technology parameters. Our NCFET-aware DVFS technique considers these effects to perform optimal voltage/frequency selection at runtime depending on workload characteristics. Results show up to 90 % energy savings compared to conventional DVFS techniques. Compared to state-of-the-art NCFET-aware power management, our technique provides up to 72 % energy savings along with 3.7x higher performance

Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits

Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that silicon-based systems cannot deliver. Nevertheless, large feature sizes in PE prohibit the realization of complex printed ML circuits. In this work, we present, for the first time, an automated printed-aware software/hardware co-design framework that exploits approximate computing principles to enable ultra-resource constrained printed multilayer perceptrons (MLPs). Our evaluation demonstrates that, compared to the state-of-the-art baseline, our circuits feature on average 6x (5.7x) lower area (power) and less than 1% accuracy loss

Thermoelectric Cooling to Survive Commodity DRAMs in Harsh Environment Automotive Electronics

Today, more and more commodity hardware devices are used in safety-critical applications, such as advanced driver assistance systems in automotive. These applications demand very high reliability of electronic components even in adverse environmental conditions, such as high temperatures. Ensuring the reliability of microelectronic components is a major challenge at these high temperatures. The computing systems of these applications rely on DRAMs as working memory, which are built upon bit cells that store charges in capacitors. These commodity DRAMs are optimized for cost per bit and not for high reliability. Thus, very high temperatures impose an enormous challenge for commodity DRAMs as the data retention time and reliability decrease largely, affecting the data correctness. Data correctness can be ensured up to certain temperatures by increasing the refresh rate to counterbalance the retention time reduction. However, this severely degrades the access latencies and the usable DRAM bandwidth. To overcome these limitations, we present for the first time a Thermoelectric Cooling (TEC) solution for commodity DRAMs in harsh-environments, such as automotive. Our TEC solution enables the use of commodity off-the-shelf DRAMs in safety-critical applications by reducing the temperature conditions to a range where they can operate reliably. This TEC solution is applied a posteriori to the DRAM chips without using high-cost package solutions. Thus, it maintains the low-cost targets of such devices, improves the reliability, and at the same time, counterbalances the adverse effects of increasing the refresh rate. To quantitatively evaluate the benefits of TEC on commodity DRAMs in harsh-environments, we performed system-level evaluations with several applications backed up by the measured data on commodity DRAMs. Our experimental results, using accurate multi-physics simulations that employ finite element method, demonstrate that the TEC-based cooling ensures that the maxim..

Impact of NCFET on Neural Network Accelerators

This is the first work to investigate the impact that Negative Capacitance Field-Effect Transistor (NCFET) brings on the efficiency and accuracy of future Neural Networks (NN). NCFET is at the forefront of emerging technologies, especially after it has become compatible with the existing fabrication process of CMOS. Neural Network inference accelerators are becoming ubiquitous in modern SoCs and there is an ever-increasing demand for tighter and tighter throughput constraints and lower energy consumption. To explore the benefits that NCFET brings to NN inference regarding frequency, energy, and accuracy, we investigate different configurations of the multiply-add (MADD) circuit, which is the core computational unit in any NN accelerator. We demonstrate that, compared to the baseline 7nm FinFET technology, its negative capacitance counterpart reduces the energy by 55%, without any frequency reduction. In addition, it enables leveraging higher computational precision, which results to a considerable improvement in the inference accuracy. Importantly, the achieved accuracy improvement comes also together with a significant energy reduction and without any loss in frequency