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 new software tool TRASIM (Two-Dimensional Transient Simulator) has been developed for arbitrary, planar semiconductor devices. A finite difference technique is employed with a modified, decoupled Gummel-like method. The memory requirements are reduced significantly compared to the conventionally used Newton-like methods. TRASIM exhibits a good stability and convergence rate, and user interaction with the computational process is significantly reduced. Low memory requirements and efficiency make the method attractive for the future 3-D applications. The software uses the drift-diffusion model, with up-to-date mobility and lifetime models. External RC-chains may be connected to device electrodes. Numerical examples are presented illustrating the advantages of the modified Gummel method over the Newton method, for transient simulation. >

TRASIM: compact and efficient two-dimensional transient simulator for arbitrary planar semiconductor devices

An automated optimization-based design strategy for 2-level MOS current mode logic (MCML) circuits is proposed to overcome the complexities of the design process. The methodology minimizes the power dissipation while satisfying the performance criteria. Moreover, environmental and process variations modeling are included in the design strategy. The impact of these variations on MCML performance as technology scales is also presented. In addition, design tips based on analytic formulation are presented for MCML designers. The proposed methodology is tested on several benchmarks belonging to optical communication and high-speed microprocessor applications built in a CMOS 0.18/spl mu/m process, at which the average error is within 7% between our formulation and HSPICE.

MOS current mode logic: design, optimization, and variability

The problem of survival memory management of a Viterbi decoder (VD) was solved by introducing a novel pointer implementation for the register exchange method, where a pointer is assigned to each row of memory in the survivor memory unit (SMU). The content of the pointer which points to one row of memory is altered to point to another row of memory, instead of copying the contents of the first row to the second. In this paper, the one-pointer VD is proposed; if the initial state of the convolutional encoder is known, the entire SMU is reduced to only one row. Because the decoded data bits are generated in the required order, even this row of memory is dispensable. Thus, the one-pointer architecture, referred to as memoryless VD (MLVD), reduces the power consumption of a traditional traceback VD by approximately 50%, but has some performance degradation. A prototype of the MLVD with a one third convolutional code rate and a constraint length of nine is mapped into a Xilinx 2V6000 chip, operating at 25 MHz with a decoding throughput of more than 3 Mbps and a latency of two data bits.

Memoryless Viterbi decoder

An overview of the optimization of buffer chains and multilevel logic in a BiCMOS environment, including scaling effects, is presented. The BiCMOS speed-up contours are reviewed. The use of these contours and analytical delay expressions in the design and optimization of BiCMOS buffer chains is also reviewed. The performance differences between different types of multi-stage mixed CMOS/BiCMOS buffers are summarized. Different BiCMOS current-mode logic (CML) circuits, such as the multi-emitter BiCMOS CML circuits, are considered. The performance advantages of using such circuits in implementing multilevel logic are summarized. >

Optimization of digital BiCMOS circuits, an overview

A speech recognizer is developed using a layered neural network to implement speech-frame prediction and using a Markov chain to modulate the network's weight parameters. The authors postulate that speech recognition accuracy is closely linked to the capability of the predictive model in representing long-term temporal correlations in data. Analytical expressions are obtained for the correlation functions for various types of predictive models (linear, nonlinear, and jointly linear and nonlinear) in order to determine the faithfulness of the models to the actual speech data. The analytical results, computer simulations, and speech recognition experiments suggest that when nonlinear and linear prediction are jointly performed within the same layer of the neural network, the model is better able to capture long-term data correlations and consequently improve speech recognition performance. >

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Mohamed I. Elmasry

Papers

TRASIM: compact and efficient two-dimensional transient simulator for arbitrary planar semiconductor devices

MOS current mode logic: design, optimization, and variability

Memoryless Viterbi decoder

Optimization of digital BiCMOS circuits, an overview

Neural-network architecture for linear and nonlinear predictive hidden Markov models: application to speech recognition