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

The demand for integrating systems on a chip functioning at high frequencies, while utilizing the recent advances of submicron IC technologies, has cost IC designers the severe problem of on-chip-crosstalk, particularly substrate coupling. This paper presents three methods for efficient modeling of substrate coupling. We develop a boundary-element method based on Green's theorem for modeling using a rapid Green's function. In addition, closed-form formulas for fast extraction of substrate parasitics in low-dense chips (disperse devices) are derived. We apply the modeling method to analysis of substrate coupling in a large RF-IC.

Modeling techniques for substrate coupling for system-on-a-chip

This paper presents a novel loss compensation technique, an active negative resistor produced by a capacitively source degenerated configuration, to improve the gain flatness and bandwidth of CMOS distributed amplifiers (DAs) This configuration provides the desired isolation from the transmission line at lower frequencies, and a frequency-increasing negative conductance that can fully compensate for the loss of the on-chip inductor over a broad frequency band A 40 GHZ three-stage 013 /spl mu/m CMOS DA is devised that outperforms previously published CMOS DAs by providing a large gain-bandwidth product of 200 GHz-dB with an improved gain-flatness of /spl plusmn/02 dB The proposed loss compensated DA dissipates 24 mW from a 12 VDC supply

A novel loss compensation technique for broadband CMOS distributed amplifiers

New high-speed low-power BiCMOS nonthreshold logic (BNTL) circuits are presented. These circuits offers a built-in CMOS and bipolar level conversion and are suitable for reduced power supply voltage. A 4-b carry lookahead generator (CLG) circuit is designed in BNTL, ECL, and CMOS using 0.8- mu m BiCMOS technology. Circuit simulations show that this new logic provides speed comparable to or better than that provided by emitter-coupled logic (ECL) for lower power dissipation. >

BiCMOS nonthreshold logic for high-speed low-power applications

This paper presents a matrix-based lumped-element theoretical analysis of CMOS distributed amplifiers that employ the image impedance technique for the input/output terminating networks. The design of distributed amplifiers can be more efficient in CMOS technology by using lumped-element modelling of the distributed amplifiers rather than the one commonly based on transmission line parameters. The simulation results clearly indicate an improved voltage gain and a better gain uniformity over the bandwidth of the proposed amplifier design. Moreover, this matrix-based lumped-element method is suitable for analysis of any distributed amplifier with an arbitrary terminating network.

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A novel matrix-based lumped-element analysis method for CMOS distributed amplifiers

In deeply pipelined synchronous systems, any violation of the timing constraints of the flip-flops can cause the overall system to malfunction. Due to CMOS technology scaling, increased process variations result in a large delay variability causing unacceptable loss in the timing yield. Several variation tolerant techniques are introduced to mitigate this variability challenge by improving the timing yield. In the mean time, devices are getting smaller, faster, and operating at lower supply voltages. These reduced capacitances and power supply voltages combined with the increased chip density to perform more functionality increase the soft errors susceptibility and make it one of the essential design challenges. Moreover, there are many flip-flops topologies that vary in their relative performance and power consumption which make the selection decision very difficult to flip-flops designers especially under variability and soft errors challenges. Therefore, a comparative analysis between these different flip-flops topologies considering these scaling challenges is beneficial to guide the flip-flops designers in selecting the best topology for their specific application constraints. This paper presents a comparative analysis of the timing yield improvement impact on flip-flops soft error rate by using the STMicroelectronics 65-nm CMOS technology. The analyzed flip-flops are compared for power and power-delay product (PDP) overheads to achieve this timing yield improvement. Then, they are compared for the soft error susceptibility. Finally, it is shown that the timing yield improvement improves the flip-flops soft error immunity significantly.

Mohamed I. Elmasry

Papers

Modeling techniques for substrate coupling for system-on-a-chip

A novel loss compensation technique for broadband CMOS distributed amplifiers

BiCMOS nonthreshold logic for high-speed low-power applications

A novel matrix-based lumped-element analysis method for CMOS distributed amplifiers

The impact of timing yield improvement under process variation on flip-flops soft error rate