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.

/pdf/differential-power-analysis-4t7fjmuc7b.pdf

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.

/pdf/deep-learning-methods-and-applications-5cyi610sjz.pdf

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.

/pdf/convolutional-neural-networks-for-speech-recognition-59lskxzlgf.pdf

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

Simultaneous multithreading (SMT) processors are widely used in high performance computing tasks. However, with the improved performance of the SMT architecture, the utilization of their functional units is significantly increased, straining the power budget of the processor. This increases not only the dynamic power consumption, but also the leakage power consumption due to the increased temperature. In this paper, a comparison of the static and dynamic sleep signal generation techniques for SMT processors is presented. This is conducted under var- ious workloads to assess their effectiveness in leakage power management. Results show that the dynamic approach exhibits a threefold increase in leakage savings, compared with that of the static approach for certain functional units.

On the Power Management of

In this chapter, we discuss device design considerations of MOS and bipolar transistors and the effect of process parameters on device parameters [Sections 3.1. and 3.2.]. The trade offs involved in designing BiCMOS are presented in Section 3.3. The study of Sections 3.1. to 3.3. is based on classical first order analyses, whose details can be found in [1, 2, 3]. The topic of device scaling has been addressed in this chapter because of the significant impact of scaling on the device design. A first order scaling theory for MOS and bipolar transistors is derived in Section 3.4.

Device Design Considerations

This paper presents a novel CMOS broadband amplification technique based on a common source circuit topology with an RC degeneration. The design needs no on-chip inductor to compensate for the high frequency response of NMOS transistors; consequently, it reduces the die area significantly, and is free from the design challenges of on-chip CMOS inductors such as low quality factor and high capacitive coupling between inductor and substrate.

A novel inductor-free broadband cmos amplification technique

Novel high speed BiCMOS circuits; ECL/CMOS, CMOS/ECL interface circuits and a BiCMOS sense amplifier are reported A generic 08pm complementary BiCMOS technology has been used in the circuit design Circuit simulation results show superior performance of the novel circuits over conventional designs

Novel High Speed Low Voltage Circuit Structures for BiCMOS SRAMs

The advancement in microprocessors and high speed digital signal processors over the past few years is attributed mainly to parallelism and the integration density of VLSI chips. This meant the integration of complete systems on a single chip with many units functioning in parallel to improve the performance and increase the throughput of the system. Parallel operation of these units requires parallel access to the data stored in memory (RAM). To facilitate such an access to a storage memory, a fast and multi-ported register file is used as a buffer between main memory and the functional units. A major drawback of a multi-ported register file is the increase in area needed to accommodate the extra ports.

Mohamed I. Elmasry

Papers

On the Power Management of

Device Design Considerations

A novel inductor-free broadband cmos amplification technique

Novel High Speed Low Voltage Circuit Structures for BiCMOS SRAMs

Low-Power Register File