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

Designing energy-efficient computing systems using equalization and machine learning

As technology scaling slows down in the nanometer CMOS regime and mobile computing becomes more ubiquitous, designing energy-efficient hardware for mobile systems is becoming increasingly critical and challenging. Although various approaches like near-threshold computing (NTC), aggressive voltage scaling with shadow latches, etc. have been proposed to get the most out of limited battery life, there is still no “silver bullet” to increasing power-performance demands of the mobile systems. Moreover, given that a mobile system could operate in a variety of environmental conditions, like different temperatures, have varying performance requirements, etc., there is a growing need for designing tunable/reconfigurable systems in order to achieve energy-efficient operation. In this work we propose to address the energy- efficiency problem of mobile systems using two different approaches: circuit tunability and distributed adaptive algorithms. Inspired by the communication systems, we developed feedback equalization based digital logic that changes the threshold of its gates based on the input pattern. We showed that feedback equalization in static complementary CMOS logic enabled up to 20% reduction in energy dissipation while maintaining the performance metrics. We also achieved 30% reduction in energy dissipation for pass-transistor digital logic (PTL) with equalization while maintaining performance. In addition, we proposed a mechanism that leverages feedback equalization techniques to achieve near optimal operation of static complementary CMOS logic blocks over the entire voltage range from near threshold supply voltage to nominal supply voltage. Using energy-delay product (EDP) as a metric we analyzed the use of the feedback equalizer as part of various sequential computational blocks. Our analysis shows that for near-threshold voltage operation, when equalization was used, we can improve the operating frequency by up to 30%, while the energy increase was less than 15%, with an overall EDP reduction of ≈10%. We also observe an EDP reduction of close to 5% across entire above-threshold voltage range. On the distributed adaptive algorithm front, we explored energy-efficient hardware implementation of machine learning algorithms. We proposed an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification hardness across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈100× more energy efficient but has ≈1% higher error rate than a complex radial basis function classifier and is ≈10× less energy efficient but has ≈40% lower error rate than a simple linear classifier across a wide range of classification data sets. We also developed a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) under tight energy budgets. The FoG architecture takes advantage of the fact that in random forests a small portion of the weak classifiers (decision trees) might be sufficient to achieve high statistical performance. By dividing the random forest into smaller forests (Groves), and conditionally executing the rest of the forest, FoG is able to achieve much higher energy efficiency levels for comparable error rates. We also take advantage of the distributed nature of the FoG to achieve high level of parallelism. Our evaluation shows that at maximum achievable accuracies FoG consumes ≈1.48×, ≈24×, ≈2.5×, and ≈34.7× lower energy per classification compared to conventional RF, SVM-RBF , Multi-Layer Perceptron Network (MLP), and CNN, respectively. FoG is 6.5× less energy efficient than SVM-LR, but achieves 18% higher accuracy on average across all considered datasets

Computing with Spintronics: Circuits and architectures

This thesis makes the following contributions towards the design of computing platforms with spintronic devices. 1) It explores the use of spintronic memories in the design of a domain-specific processor for an emerging class of data-intensive applications, namely recognition, mining and synthesis (RMS). Two different spintronic memory technologies — Domain Wall Memory (DWM) and STT-MRAM — are utilized to realize the different levels in the memory hierarchy of the domain-specific processor, based on their respective access characteristics. Architectural tradeoffs created by the use of spintronic memories are analyzed. The proposed design achieves 1.5X-4X improvements in energy-delay product compared to a CMOS baseline. 2) It describes the first attempt to use DWM in the cache hierarchy of general-purpose processors. DWM promises unparalleled density by packing several bits of data into each bit-cell. TapeCache, the proposed DWM-based cache architecture, utilizes suitable circuit and architectural optimizations to address two key challenges (i) the high energy and latency requirement of write operations and (ii) the need for shift operations to access the data stored in each DWM bit-cell. At the circuit level, DWM bit-cells that are tailored to the distinct design requirements of different levels in the cache hierarchy are proposed. At the architecture level, TapeCache proposes suitable cache organization and management policies to alleviate the performance impact of shift operations required to access data stored in DWM bit-cells. TapeCache achieves more than 7X improvements in both cache area and energy with virtually identical performance compared to an SRAM-based cache hierarchy. 3) It investigates the design of the on-chip memory hierarchy of general-purpose graphics processing units (GPGPUs)—massively parallel processors that are optimized for data-intensive high-throughput workloads—using DWM. STAG, a high density, energy-efficient Spintronic- Tape Architecture for GPGPU cache hierarchies is described. STAG utilizes different DWM bit-cells to realize different memory arrays in the GPGPU cache hierarchy. To address the challenge of high access latencies due to shifts, STAG predicts upcoming cache accesses by leveraging unique characteristics of GPGPU architectures and workloads, and prefetches data that are both likely to be accessed and require large numbers of shift operations. STAG achieves 3.3X energy reduction and 12.1% performance improvement over CMOS SRAM under iso-area conditions. 4) While the potential of spintronic devices for memories is widely recognized, their utility in realizing logic is much less clear. The thesis presents Spintastic, a new paradigm that utilizes Stochastic Computing (SC) to realize spintronic logic. In SC, data is encoded in the form of pseudo-random bitstreams, such that the probability of a \u271\u27 in a bitstream corresponds to the numerical value that it represents. SC can enable compact, low-complexity logic implementations of various arithmetic functions. Spintastic establishes the synergy between stochastic computing and spin-based logic by demonstrating that they mutually alleviate each other\u27s limitations. On the one hand, various building blocks of SC, which incur significant overheads in CMOS implementations, can be efficiently realized by exploiting the physical characteristics of spin devices. On the other hand, the reduced logic complexity and low logic depth of SC circuits alleviates the shortcomings of spintronic logic. Based on this insight, the design of spin-based stochastic arithmetic circuits, bitstream generators, bitstream permuters and stochastic-to-binary converter circuits are presented. Spintastic achieves 7.1X energy reduction over CMOS implementations for a wide range of benchmarks from the image processing, signal processing, and RMS application domains. 5) In order to evaluate the proposed spintronic designs, the thesis describes various device-to-architecture modeling frameworks. Starting with devices models that are calibrated to measurements, the characteristics of spintronic devices are successively abstracted into circuit-level and architectural models, which are incorporated into suitable simulation frameworks. (Abstract shortened by UMI.

Journal of Telecommunications and Information Technology, 2007, nr 2

kwartalni

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Cutting Edge Nanotechnology

The main purpose of this book is to describe important issues in various types of devices ranging from conventional transistors (opening chapters of the book) to molecular electronic devices whose fabrication and operation is discussed in the last few chapters of the book. As such, this book can serve as a guide for identifications of important areas of research in micro, nano and molecular electronics. We deeply acknowledge valuable contributions that each of the authors made in writing these excellent chapters

GSI Scientific Report 2009 [GSI Report 2010-1]

Displacement design response spectrum is an essential component for the currently-developing displacement-based seismic design and assessment procedures. This paper proposes a new and simple method for constructing displacement design response spectra on soft soil sites. The method takes into account modifications of the seismic waves by the soil layers, giving due considerations to factors such as the level of bedrock shaking, material non-linearity, seismic impedance contrast at the interface between soil and bedrock, and plasticity of the soil layers. The model is particularly suited to applications in regions with a paucity of recorded strong ground motion data, from which empirical models cannot be reliably developed