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

Leakage power is projected to comprise approximately 50% of the processor?s power for sub 65nm technologies. Much of this power is consumed in the processor?s functional units. Accordingly, leakage control techniques are employed to reduce leakage in these functional units. Many of these techniques are dynamic and are based on an input sleep signal to initiate a low leakage mode. However, since most of these leakage control techniques are based on circuit level schemes, such techniques inherently lack information about the operational profile of the functional units they manage. This limitation is usually handled statically by using a fixed length counter that generates the sleep signal when the functional unit is idle for a specified number of cycles. In this paper, the limitations of the static sleep signal generation approach are identified, and the use of a dynamic alternative that is capable of adopting the counter length to the running application is proposed. In order to assess the accuracy of the proposed dynamic sleep signal generator, the length of the sleep period following the sleep signal generation is used as a metric to identify the usefulness of utilizing the dynamic approach. Experimental results for the dynamic alternative shows up to 98% accuracy in predicting the length of the standby period compared to an average of 40 . 60% in the static case, which translates into increased leakage savings. This is achieved while consuming 360 ?W of overhead power at 1 GHz.

Dynamic Standby Prediction for Leakage Tolerant Microprocessor Functional Units

An 8-stage distributed amplifier (DA) suitable for 40Gb/s optical communication is implemented in a 0.13mum CMOS process. The losses of on-chip transmission lines are compensated by active negative resistors. The DA achieves a flat gain of 10dB from DC to 44GHz with an input and output matching better than -8dB. The core DA and loss compensation circuitry dissipate 44mW and 59mW, respectively.

A 10dB 44GHz Loss-Compensated CMOS Distributed Amplifier

A generalized first-order scaling theory for BiCMOS digital circuit structures is presented. The effect of horizontal, vertical, and voltage scaling on the speed performance of various BiCMOS circuits is presented. The generalized scaling theory is used for the MOSFET, and the constant collector current (CIC) scaling scheme is used for the bipolar junction transistor (BJT). In scaling the bipolar transistor, polysilicon emitter contact and bandgap narrowing are taken into account. A case study for scaling BiCMOS circuits operating at 5- and 3.3-V power supplies shows that scaling improved BiCMOS buffers more significantly than CMOS buffers. Moreover, the low delay-to-load sensitivity of BiCMOS is preserved with scaling. >

Scaling of digital BiCMOS circuits

https://www.microsoft.com/en-us/research/wp-content/uploads/1994/01/Deng-NN-1994.pdf

Analysis of the correlation structure for a neural predictive model with application to speech recognition

A direct adaptive body bias (D-ABB) circuit is proposed in this paper. The D-ABB is used to compensate for die-to-die (D2D) and within-die (WID) parameter variations, and accordingly, improves the circuit yield regarding the speed, the dynamic power, and the leakage power. The D-ABB circuit consists of threshold voltage estimation circuits and direct control of the body bias performed by on-chip direct controller circuits. Circuit level simulation results of a circuit block case study, extracted from a real microprocessor critical path, referring to an industrial hardware-calibrated 65-nm CMOS technology transistor model, are presented. These results show that the proposed D-ABB reduces the standard deviations of the frequency, the dynamic power, and the leakage power by factors of 5.5×, 6.4×, and 4.5×, respectively, when both D2D and WID variations are considered. In addition, in the presented case study, initial total yields of 16.8% and 13% are improved to 100% and 91.4%, respectively. The proposed D-ABB circuit exhibits lower area overhead compared to the other ABB circuits reported in the literature.

Mohamed I. Elmasry

Papers

Dynamic Standby Prediction for Leakage Tolerant Microprocessor Functional Units

A 10dB 44GHz Loss-Compensated CMOS Distributed Amplifier

Scaling of digital BiCMOS circuits

Analysis of the correlation structure for a neural predictive model with application to speech recognition

A Novel Low Area Overhead Direct Adaptive Body Bias (D-ABB) Circuit for Die-to-Die and Within-Die Variations Compensation