Cryptosystem designers frequently assume that secrets will be manipulated in closed, reliable computing environments. Unfortunately, actual computers and microchips leak information about the operations they process. This paper examines specific methods for analyzing power consumption measurements to find secret keys from tamper resistant devices. We also discuss approaches for building cryptosystems that can operate securely in existing hardware that leaks information.

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Differential Power Analysis

This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.

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Deep Learning: Methods and Applications

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Recently, the hybrid deep neural network (DNN)- hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction can be obtained by using convolutional neural networks (CNNs). We first present a concise description of the basic CNN and explain how it can be used for speech recognition. We further propose a limited-weight-sharing scheme that can better model speech features. The special structure such as local connectivity, weight sharing, and pooling in CNNs exhibits some degree of invariance to small shifts of speech features along the frequency axis, which is important to deal with speaker and environment variations. Experimental results show that CNNs reduce the error rate by 6%-10% compared with DNNs on the TIMIT phone recognition and the voice search large vocabulary speech recognition tasks.

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Convolutional neural networks for speech recognition

From the Publisher:
Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e., HMM emission probability estimation and feature extraction. The book describes a successful five year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical system. Using standard databases and comparing with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition: A Hybrid Approach is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. This book is also suitable as a text for advanced courses on neural networks or speech processing.

Connectionist Speech Recognition: A Hybrid Approach

A design methodology for automated mapping of DSP algorithms into VLSI architectures is presented. The methodology takes into account explicit algorithm requirements on throughput and latency, in addition to VLSI technology constraints on silicon area and power dissipation. Algorithm structure, design style of functional units, and parallellism of the architecture are all explored in the design space. The synthesized architecture is a multibus multifunction unit processor matched to the implemented algorithm. The architecture has a linear topology and uses a lower number of interconnects and multiplexer inputs compared to other synthesized architectures with random topology having the same performance. The synthesized processor is a self-timed element externally, while it is internally synchronous. The methodology is implemented in a design aid tool called SPAID. Results obtained using SPAID for two DSP algorithms compare favorably with other synthesis techniques. >

Architectural synthesis for DSP silicon compilers

A new high-speed domino circuit, called HS-Domino is developed. HS-Domino resolves the trade-off between performance and noise margins in conventional CD-Domino logic while dissipating low dynamic power with minimal area overhead. A dual-threshold (MTCMOS) implementation of HS-Domino and DDCVS logic is also devised. This implementation achieves low leakage values during standby, while maintaining high performance and low dynamic power during the active mode.

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High-speed dynamic logic styles for scaled-down CMOS and MTCMOS technologies

STAIC is an interactive design tool that synthesizes CMOS and BiCMOS analog integrated circuits that conform to specified performance constraints. STAIC features an input modeling language for entering hierarchical circuit descriptions and a symbolic/numeric solve unit that dynamically integrates analytical model equations across hierarchical boundaries. The output of the solver is a flattened homogeneous model that is customized to a user-specified topology and set of performance specifications. The output is thus tailored for optimization and other numerically intense design exploration procedures. All model descriptions include physical layout so that important net parasitics may be fully accounted for during design evaluation. Synthesis proceeds via a successive solution refinement methodology. Multilevel models of increasing sophistication are used by scan and optimization modules to converge to what is likely a globally optimal solution. Design experiments have shown that STAIC can produce satisfactory results. >

STAIC: an interactive framework for synthesizing CMOS and BiCMOS analog circuits

An integer programming (IP) model, which simultaneously schedules and allocates functional units, registers, and busses, is presented for synthesizing cost-constrained globally optimal architectures. This research is important for industry by providing optimal schedules which minimize interconnect costs and interface to analog and asynchronous processes, since these are seen as key to synthesizing high performance architectures. A partially structured tight IP formulation of the architectural synthesis problem provides globally optimal schedules for peicewise linear cost functions, using branch and bound, in execution times faster than previous research. This research breaks new ground by 1. simultaneously scheduling and allocating hardware resources including interconnect, 2. support for asynchronous and analog interfaces, and 3. guaranteeing globally optimal solutions in practical execution times.

Simultaneous scheduling and allocation for cost constrained optimal architectural synthesis

In this paper, the main challenges of technology scaling are reviewed in depth Five popular logic families namely; conventional CMOS, complementary pass logic, Domino, differential cascode voltage switch logic, and current mode logic are presented, highlighting their advantages and drawbacks The behavior of each logic style in deep submicrometer technologies is analyzed and predicted for future technology generations To verify the qualitative analysis, simulations were performed on the basic logic gates, full adder and a 16-bit carry look ahead adder The circuits were implemented in 08-, 06-, 035-, and 025-/spl mu/m CMOS technologies, and optimized for minimum energy-delay product

Mohamed I. Elmasry

Papers

Architectural synthesis for DSP silicon compilers

High-speed dynamic logic styles for scaled-down CMOS and MTCMOS technologies

STAIC: an interactive framework for synthesizing CMOS and BiCMOS analog circuits

Simultaneous scheduling and allocation for cost constrained optimal architectural synthesis

Impact of technology scaling on CMOS logic styles