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 novel technique for a self-equalized distributed amplifier is presented by showing the analogy between transversal filters and distributed amplifier topologies. The appropriate delay and gain coefficients of the amplifier circuit are obtained by Fourier expansion of the raised cosine shape.

A linear phase distributed amplifier for wide band optical transceiver using transversal filter concept

This paper presents a unified model for delay estimation in various CMOS logic styles. It also derives closed-form optimal transistor sizing formulas for minimizing the delay in each logic style. The paper demonstrates the use of these formulas for delay optimization in mixed logic-style CMOS circuits. Mixing CMOS logic styles in a circuit has the potential of improving performance and reducing energy dissipation and area.

Estimation and optimization of delay in popular CMOS logic styles

Artificial Neural Networks (ANNs) are successfully applied to a variety of data classification and recognition problems. Through experimentation and simulation, acceptable solutions to such problems can be obtained using ANNs. However, the effectiveness of an ANN algorithm strongly depends on the hardware that executes it. This hardware has to capture the inherent parallelism of the ANNs and tolerate the ANN’s need of massive numbers of computations.

A Sampled-Data CMOS VLSI Implementation of a Multi-Character ANN Recognition System

Digital signal processing (DSP) is the technology at the heart of the next generation of personal communication systems. Most DSP systems incorporate a multiplication unit to implement algorithms such as convolution and filtering. In many DSP algorithms, the multiplier lies in the critical delay path and ultimately determines the performance of the algorithm. Figure 3.1 shows an implementation of the discrete cosine transform, an algorithm widely used in image and video compression. This particular implementation requires 32 convolutions and 8 additions [1]. Thus, improving the throughput of this algorithm requires a high-performance multiplier. Traditionally in order to achieve high performance multipliers, parallel addition of the partial products is used along with reducing the technology feature size.

Low-Power High-Performance Multipliers

Several computer-aided methodologies have been proposed in the literature to optimally design MTCMOS circuits Some of those methodologies employed high-V th switch sleep transistors, whereas others have involved embedded high-V th transistors or gates This chapter presents CAD methods to optimally design MTCMOS circuits with embedded high-V th transistors or gates

Mohamed I. Elmasry

Papers

A linear phase distributed amplifier for wide band optical transceiver using transversal filter concept

Estimation and optimization of delay in popular CMOS logic styles

A Sampled-Data CMOS VLSI Implementation of a Multi-Character ANN Recognition System

Low-Power High-Performance Multipliers

Embedded MTCMOS Combinational Circuits