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

This paper introduces a new reduced swing logic style called dynamic current mode logic (DyCML) that reduces both gate and interconnect power dissipation. DyCML circuits combine the advantages of MOS current mode logic (MCML) circuits with those of dynamic logic families to achieve high performance at a low-supply voltage with low-power dissipation. Unlike CML circuits, DyCML gates do not have a static current source, which makes DyCML a good candidate for portable devices and battery-powered systems. Simulation and test results show that DyCML circuits are superior to other logic styles in terms of power and delay. A 16-bit DyCML carry look-ahead adder (CLA), fabricated in 0.6-/spl mu/m CMOS technology, attains a delay of 1.24 ns and dissipates 19.2 mW at 400 MHz.

Dynamic current mode logic (DyCML): a new low-power high-performance logic style

A new high-speed domino circuit, called HS-Domino has been developed. HS-Domino resolves the tradeoff between performance and reliability in conventional CD-domino logic while dissipating low dynamic power with minimal area overhead. HS-Domino, therefore, extends domino's operation in the deep submicron regime. A multithreshold implementation of HS-Domino is also devised to achieve substantially low leakage values during standby, while maintaining high performance and low power during the active mode. Furthermore, the generic multithreshold scheme is applied to differential cascode voltage switch (DDCVS) logic.

Energy-efficient noise-tolerant dynamic styles for scaled-down CMOS and MTCMOS technologies

Various applications have demonstrated that asynchronous circuits have great potential for energy-efficient and high-performance design. One of the primitives used in asynchronous control circuits is the C-element. Analytical delay and energy models are presented and applied to the most popular complementary metal-oxide-semiconductor (CMOS) implementations of the C-element. Optimization of these implementations are discussed. The implementations are also compared using simulations. The simulation results are in good agreement with the analytical predictions.

Modeling and comparing CMOS implementations of the C-element

The interest in MOS current-mode logic (MCML) is increasing because of its ability to dissipate less power than conventional CMOS circuits at high frequencies, while providing an analog friendly environment. Moreover, automated design methodologies are gaining attention by circuit designers to provide shorter design cycles and faster time to market. This paper provides designers with an insight to the different tradeoffs involved in the design of MCML circuits to efficiently and systematically design MCML circuits. A comprehensive analytical formulation for the design parameters of MCML circuits using the BSIM3v3 model is introduced. In addition, a closed-form expression for the noise margin of two-level MCML circuits is derived. In order to verify the validity of the analytical formulations, an automated design methodology for MCML circuits is proposed to overcome the complexities of the design process. The effectiveness of the design methodology and the accuracy of the analytical formulations are tested by designing several MCML benchmarks built in a 0.18-/spl mu/m CMOS technology. The error in the required performance in the designed circuits is within 11% when compared to HSPICE simulations. A worst case parameter variations modeling is presented to investigate the impact of variations on MCML circuits as well as designing MCML circuits for variability. Finally, the impact of variations on MCML circuits is investigated with technology scaling and different circuit architectures.

MOS current mode circuits: analysis, design, and variability

A relaxed linear programming model which simultaneously schedules and allocates functional units and registers is presented for synthesizing cost-constrained globally optimal architectures. This approach is important for industrial applications, because it provides exploration of optimal synthesized architectures and early architectural decisions have the greatest impact on the final design. An integer programming formulation of the architectural synthesis problem is transformed into the mode packing problem. Polyhedral theory is used to formulate constraints that decrease the size of the search space, thus improving solution efficiency. Execution times are an order of magnitude faster than for previous heuristic techniques. The present approach breaks new ground by (1) simultaneously scheduling and allocating in practical execution times, (2) guaranteeing globally optimal solutions for a specific objective function, and (3) providing a polynomial run-time algorithm for solving some instances of this NP-complete problem. >

Mohamed I. Elmasry

Papers

Dynamic current mode logic (DyCML): a new low-power high-performance logic style

Energy-efficient noise-tolerant dynamic styles for scaled-down CMOS and MTCMOS technologies

Modeling and comparing CMOS implementations of the C-element

MOS current mode circuits: analysis, design, and variability

A global optimization approach for architectural synthesis