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

1. Introduction.- References.- 2. Leakage Power: Challenges and Solutions.- 2.1 Introduction.- 2.2 Power Dissipation in CMOS Digital Circuits.- 2.3 Impact of Technology Scaling on Leakage Power.- 2.4 (Vdd-Vth) Design Space.- 2.5 Total Power Management.- 2.6 Leakage Power Control Circuit Techniques.- 2.7 Chapter Summary.- References.- 3. Embedded Mtcmos Combinational Circuits.- 3.1 Introduction.- 3.2 Basic Concept.- 3.3 The Power Minimization Problem.- 3.4 Algorithms.- 3.5 Choosing the High-Vth Value.- 3.6 Chapter Summary.- References.- 4. Mtcmos Combinational Circuits Using Sleep Transistors.- 4.1 Introduction.- 4.2 MTCMOS Design: Overview.- 4.3 Variable Breakpoint Switch Level Simulator [1].- 4.4 Hierarchical Sizing Based on Mutually Exclusive Discharge Patterns.- 4.5 Designing High-Vth Sleep Transistors, the Average Current Method [6].- 4.6 Drawbacks of Techniques.- 4.7 Distributed Sleep Transistors [9] [10].- 4.8 Clustering Techniques.- 4.9 Hybrid Heuristic Techniques.- 4.10 Virtual Ground Bounce.- 4.11 Results: Taking ground bounce into account.- 4.12 Power Management of Sleep Transistors.- 4.13 Chapter Summary.- References.- 5. Mtcmos Sequential Circuits.- 5.1 Introduction.- 5.2 MTCMOS Latch Circuit.- 5.3 MTCMOS Balloon Circuit.- 5.4 Intermittent Power Supply Scheme.- 5.5 Auto-Backgate-Controlled MTCMOS.- 5.6 Virtual Rails Clamp (VRC) Circuit.- 5.7 Leakage Sneak Paths in MTCMOS Sequential Circuits.- 5.8 Interfacing MTCMOS and CMOS blocks.- 5.9 Impact of the High-Vth and Low-Vth values on MTCMOS Sequential Circuit Design.- 5.10 Leakage Feedback Gates.- 5.11 Chapter Summary.- References.- 6. Mtcmos Dynamic Circuits.- 6.1 Introduction.- 6.2 Clock-Delayed Domino Logic: Overview.- 6.3 HS-Domino Logic.- 6.4 MTCMOS CD-Domino Logic: Analysis and Overview.- 6.5 MTCMOS HS-Domino (MHS-Domino) Logic.- 6.6 Domino Dual Cascode Voltage Switch Logic (DDCVSL).- 6.7 Chapter Summary.- References.- 7. Mtcmos Current-Steering Circuits.- 7.1 MOS Current Mode Logic: Overview.- 7.2 Introduction.- 7.3 Minimum Supply Voltage: First Constraint.- 7.4 Saturation Assurance: Second Constraint and the Proposed MTCMOS Design.- 7.5 A 2.5 Gbit/s 1:8 Demultiplexer in MTCMOS MCML.- 7.6 Impact of Using MTCMOS Technology Over MCML Parameters.- 7.7 Chapter Summary.- References.

Multi-Threshold CMOS Digital Circuits: Managing Leakage Power

In this work, a new termination technique for the averaging network of the flash analog-to-digital converter (ADC) input preamplifiers is devised. The proposed technique eliminates the over-range voltage headroom consumed by the dummy preamplifiers and therefore, the input capacitance and power dissipation of the ADC is reduced. This technique is applied to the design of a 6-bit 1.6-GS/s flash ADC in 0.13-mum CMOS technology. The measured peak INL and DNL are 0.42 LSB and 0.49 LSB, respectively. The ADC achieves an effective resolution bandwidth (ERBW) of 800 MHz and an SNDR of 30 dB at 1.45-GHz input signal frequency while consuming 180 mW.

A 6-Bit 1.6-GS/ s Low-Power Wideband Flash ADC Converter in 0.13- $\mu$ m CMOS Technology

A VLSI design synthesis approach with testability, area, and delay constraints is presented. This research differs from other synthesizers by implementing testability as part of the VLSI design solution. A binary tree data structure is used throughout the testable design search. Its bottom up and top down tree algorithms provide datapath allocation, constraint estimation, and feedback for design exploration. The partitioning and two dimensional characteristics of the binary tree structure provide VLSI design floorplans and global information for test incorporation. An elliptical wave filter example was used to illustrate the design synthesis with testability constraints methodology. Test methodologies such as multiple chain scan paths and BIST with different test schedules were explored. Design Scores comprised of area, delay, fault coverage, and test length were computed and graphed. Results show that the 'best' testable design solution is not always the same as that obtained from the 'best' design solution of an area and delay based synthesis search.

VLSI design synthesis with testability

One of the most frequently used primitives in asynchronous control circuits is the C-element. The three most popular CMOS implementations of the C-element are compared with respect to energy-efficiency, delay, and area, with an emphasis on energy. The three implementations have been introduced by Sutherland, Martin, and Van Berkel. We show that in a typical environment, Van Berkel's implementation is superior to the other implementations with respect to energy consumption and area, for the same delay.

A comparison of CMOS implementations of an asynchronous circuits primitive: the C-element

I: Introduction.- 1. Global VLSI Design Cycle.- 1.1 VLSI Design Cycle.- 1.2 Hybrid Systems Design.- 1.3 Impact Of Technologies.- 1.4 Test Considerations.- 1.5 Bottlenecks and Open Issues.- 1.6 focus of Text.- 2. Behavioral and Structural Interfaces.- 2.1 Input to an Architectural Synthesizer.- 2.2 Interface Primitives for External Processes.- 2.3 Output Primitives from an Architectural Synthesizer.- II: Review And Background.- 3. State of the Art Synthesis.- 3.1 Terminology and Subtask Definitions.- 3.2 High Level Transformations.- 3.3 Independent Subtask Optimization.- 3.3.1 Scheduling.- 3.3.2 Resource Allocation.- 3.4 Iterative and Simultaneous Approaches.- 3.5 Mathematical Approaches.- 3.5.1 Branch and Bound.- 3.5.2 Simulated Annealing.- 3.5.3 Makespan Scheduling.- 3.5.4 Feasibility Models.- 3.6 Timing Constrained Synthesis.- 3.7 Cost Functions for Design Evaluation.- 4. Introduction to Integer Programming.- 4.1 Applications and Models.- 4.2 Solution of Unstructured IPs.- 4.3 Polyhedral Approaches To Solving Ips.- 4.4 The Node Packing Problem.- 4.5 The Knapsack Problem.- III: Optimal Architectural Synthesis With Interfaces.- 5. A Methodology for Architectural Synthesis.- 5.1 Requirements for High Level Synthesis Tools.- 5.2 High Level Methodology.- 5.3 OASIC Methodology.- 5.4 An Introduction to OASIC.- 5.5 Oasic Terminology, Assumptions, and Preprocessing.- 5.5.1 terminology.- 5.5.2 Assumptions.- 5.5.3 Preprocessing.- 6. Simultaneous Scheduling, and Selection and Allocation Of Functional Units.- 6.1 The Formal Model.- 6.2 Cost Functions.- 6.3 Functional Unit Type Selection.- 7. Oasic: Area-Delay Constrained Architectural Synthesis.- 7.1 The Precedence Constrained Scheduling Model.- 7.2 Functional Unit Allocation.- 7.3 Register Allocation.- 7.4 Bus Allocation.- 7.5 Cost Functions.- 7.6 Application Specific Tightening of Con-straints.- 8. Support for Algorithmic Constructs.- 8.1 Conditional Code.- 8.2 Loops.- 8.3 Functional Pipelining.- 9. Interface Constraints.- 9.1 General Interface: Minimum and Max-imum Timing Constraints.- 9.2 Analog Interface: Fixed Timing Con-straint.- 9.3 Asynchronous Interface.- 9.4 Unknown Unbounded Delays.- 9.5 Complex Timing Constraints.- 10. Oasic Synthesis Results.- 10.1 Elliptical Wave Filter.- 10.1.1 Structured Model.- 10.1.2 Area-delay Optimized.- 10.2 Neural Network Algorithm.- 10.3 Conditional Code Example.- 10.4 Analog and Asynchronous interface Examples.- 10.6.1 Analog Interface.- 10.6.2 Asynchronous Interface.- IV: Testable Architectural Synthesis.- 11. Testability in Architectural Synthesis.- 11.1 Design and Test.- 11.1.1 Choices in Design and Test.- 11.2 Approaches to Testability.- 11.2.1 Test Measures and Tools.- 11.2.2 Design Modifications for Testability.- 11.3 Previous Research in Design for Test.- 11.4 Approaches To Test With Synthesis.- 11.4.1 Previous Research.- 11.4.2 Commercial Systems.- 11.5 Inadequacies of Current Synthesis With Test.- 11.5.1 Feedback.- 11.5.2 Integration.- 11.5.3 Constraint Estimation.- 12. The Catree Architectural Synthesis With Testability.- 12.1 problem description.- 12.2 Comparison With Previous Research.- 12.3 Two Synthesis With Test Methodologies:Catree & Catree2.- 12.4 Catree Design Synthesis Stages.- 12.4.1 Input Specification.- 12.4.2 Design Allocation.- 12.4.3 Catree Area and Delay Estimates.- 12.4.4 Test Incorporation.- 12.4.5 Feedback.- 12.5 Catree Synthesis Results.- 12.6 Catree Discussion.- 12.7 Catree2 Design Synthesis stages.- 12.7.1 Tree Formation and Functional Unit Binding.- 12.7.2 Test Incorporation.- 12.7.3 (Test) Register and Bus Binding.- 12.7.4 Feedback.- 12.8 Catree2 Experiments.- 12.9 Catree2 Discussion.- V: Summary and Future Research.- 13. Summary and Future Research.- 13.1 Oasic Summary.- 13.2 Catree Summary.- 13.3 Future Extensions.- 13.4 Concluding Remarks.- References.

Mohamed I. Elmasry

Papers

Multi-Threshold CMOS Digital Circuits: Managing Leakage Power

A 6-Bit 1.6-GS/ s Low-Power Wideband Flash ADC Converter in 0.13- $\mu$ m CMOS Technology

VLSI design synthesis with testability

A comparison of CMOS implementations of an asynchronous circuits primitive: the C-element

Optimal VLSI Architectural Synthesis: Area, Performance and Testability