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

In this paper we present circuit techniques for CMOS low-power high-performance multiplier design. Novel full adder circuits were simulated and fabricated using 0.8-/spl mu/m CMOS (in BiCMOS) technology. The complementary pass-transistor logic-transmission gate (CPL-TG) full adder implementation provided an energy savings of 50% compared to the conventional CMOS full adder. CPL implementation of the Booth encoder provided 30% power savings at 15% speed improvement compared to the static CMOS implementation. Although the circuits were optimized for (16/spl times/16)-b multiplier using the Booth algorithm, a (6/spl times/6)-b implementation was used as a test vehicle in order to reduce simulation time. For the (6/spl times/6)-b case, implementation based on CPL-TG resulted in 18% power savings and 30% speed improvement over conventional CMOS.

Circuit techniques for CMOS low-power high-performance multipliers

Reducing power dissipation is one of the most important issues in very large scale integration design today. Scaling causes subthreshold leakage currents to become a large component of total power dissipation. Multithreshold technology has emerged as a promising technique to reduce leakage power. This paper presents several heuristic techniques for efficient gate clustering in multithreshold CMOS circuits by modeling the problem via bin-packing (BP) and set-partitioning (SP) techniques. The SP technique takes the circuit's routing complexity into consideration which is critical for deep submicron (DSM) implementations. By applying the techniques to six benchmarks to verify functionality, results obtained indicate that our proposed techniques can achieve on average 84% savings for leakage power and 12% savings for dynamic power. Furthermore, four hybrid clustering techniques that combine the BP and SP techniques to produce a more efficient solution are also devised. Ground bounce was also taken as a design parameter in the optimization problem. While accounting for noise, the proposed hybrid solution achieves on average 9% savings for dynamic power and 72% savings for leakage power dissipation at sufficient speeds and adequate noise margins.

Design and optimization of multithreshold CMOS (MTCMOS) circuits

This paper introduces a simple analytical model for estimating standby and switching power dissipation in deep submicron CMOS digital circuits. The model is based on Berkeley Short-Channel IGFET model and fits HSPICE simulation results well. Static and dynamic power analysis for various threshold voltages is addressed. A design methodology to minimize the power-delay product by selecting the lower and upper bounds of the supply and threshold voltages is presented. The effects of the supply voltage, the threshold voltage, and /spl eta/, which reflects the drain induced barrier lowering, are also addressed.

Power dissipation analysis and optimization of deep submicron CMOS digital circuits

Reducing power dissipation is one of the most principle subjects in VLSI design today. Scaling causes subthreshold leakage currents to become a large component of total power dissipation. This paper presents two techniques for efficient gate clustering in MTCMOS circuits by modeling the problem via Bin-Packing (BP) and Set-Partitioning (SP) techniques. An automated solution is presented, and both techniques are applied to six benchmarks to verify functionality. Both methodologies offer significant reduction in both dynamic and leakage power over previous techniques during the active and standby modes respectively. Furthermore, the SP technique takes the circuit's routing complexity into consideration which is critical for Deep Sub-Micron (DSM) implementations. Sufficient performance is achieved, while significantly reducing the overall sleep transistors' area. Results obtained indicate that our proposed techniques can achieve on average 90% savings for leakage power and 15% savings for dynamic power.

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Dynamic and leakage power reduction in MTCMOS circuits using an automated efficient gate clustering technique

A low-power direct digital frequency synthesizer (DDFS) architecture is presented. It uses a smaller lookup table for sine and cosine functions compared to already existing systems with a minimum additional hardware. Only 16 points are stored in the internal memory implemented in ROM (read-only memory). The full computation of the generated sine and cosine is based on the linear interpolation between the sample points. A DDFS with 60-dBc spectral purity 29-Hz frequency resolution, and 9-bit output data for sine function generation is being implemented in 0.8-/spl mu/m CMOS technology. Experimental results verify that the average power dissipation of the DDFS logic is 9.5 mW (at 30 MHz, 3.3 V).

Mohamed I. Elmasry

Papers

Circuit techniques for CMOS low-power high-performance multipliers

Design and optimization of multithreshold CMOS (MTCMOS) circuits

Power dissipation analysis and optimization of deep submicron CMOS digital circuits

Dynamic and leakage power reduction in MTCMOS circuits using an automated efficient gate clustering technique

Low-power direct digital frequency synthesis for wireless communications