Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

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

With advanced computer technology, systems are getting larger to fulfill more complicated tasks: however, they are also becoming less reliable. Consequently, testing is an indispensable part of system design and implementation; yet it has proved to be a formidable task for complex systems. This motivates the study of testing finite stare machines to ensure the correct functioning of systems and to discover aspects of their behavior. A finite state machine contains a finite number of states and produces outputs on state transitions after receiving inputs. Finite state machines are widely used to model systems in diverse areas, including sequential circuits, certain types of programs, and, more recently, communication protocols. In a testing problem we have a machine about which we lack some information; we would like to deduce this information by providing a sequence of inputs to the machine and observing the outputs produced. Because of its practical importance and theoretical interest, the problem of testing finite state machines has been studied in different areas and at various times. The earliest published literature on this topic dates back to the 1950's. Activities in the 1960's mid early 1970's were motivated mainly by automata theory and sequential circuit testing. The area seemed to have mostly died down until a few years ago when the testing problem was resurrected and is now being studied anew due to its applications to conformance testing of communication protocols. While some old problems which had been open for decades were resolved recently, new concepts and more intriguing problems from new applications emerge. We review the fundamental problems in testing finite state machines and techniques for solving these problems, tracing progress in the area from its inception to the present and the stare of the art. In addition, we discuss extensions of finite state machines and some other topics related to testing.

Principles and methods of testing finite state machines-a survey

Ultrathin films of single-walled carbon nanotubes (SWNTs) represent an attractive, emerging class of material, with properties that can approach the exceptional electrical, mechanical, and optical characteristics of individual SWNTs, in a format that, unlike isolated tubes, is readily suitable for scalable integration into devices. These features suggest the potential for realistic applications as conducting or semiconducting layers in diverse types of electronic, optoelectronic and sensor systems. This article reviews recent advances in assembly techniques for forming such films, modeling and experimental work that reveals their collective properties, and engineering aspects of implementation in sensors and in electronic devices and circuits with various levels of complexity. A concluding discussion provides some perspectives on possibilities for future work in fundamental and applied aspects.

http://www.ifc.dicp.ac.cn/library/cailiao/pdf1/11.pdf

Ultrathin Films of Single-Walled Carbon Nanotubes for Electronics and Sensors: A Review of Fundamental and Applied Aspects

This paper presents analytical models for evaluating intermediate tests for yield enhancement and quality assurance of systems manufactured using fault-tolerant multichip modules (MCM's) for massively parallel computing (MPC). In the proposed approaches, we employ both a novel Markov model and a so-called working-test-set to compute the yield. Unlike a previous method [1] which utilizes a binomial distribution, our scheme can employ intermediate tests to meet MCM quality requirements effectively. Several strategies for appropriately testing fault-tolerant MCM's have been proposed, but little analytical evaluation has been performed. In this paper, it is shown that an efficient test strategy with a modest level of redundancy may exist to achieve virtually 100% first-pass MCM yield for a particular system. We note that a yield-analysis model employing the LRTWS (Least Recently Tested in WS) test strategy proposed in this paper may provide a very good figure of merit due to its cost, delivery, number of tests and reliability benefits for current technology. Extensive parametric results for the analysis are provided to show that our approach can be applied to calculate the overall yield for fault-tolerant MCM's more accurately and efficiently, thereby improving upon the reliability and quality of the entire system.

Modeling intermediate tests for fault-tolerant multichip module systems

Embedded computers commonly rely on multiple-board systems, called tristate system environments. These environments consist of an interconnect and drivers or receivers with tristate features and boundary scan capabilities. The authors present a comprehensive fault model that provides 100 percent fault coverage and minimizes test set size.

Fault detection in a tristate system environment

Deep learning requires powerful designs to process large volume of data during inference while meeting other metrics of high performance such as efficient hardware, operating frequency, power dissipation and latency. In this paper, the shortcomings of current technologies in implementing the forward propagation of Multi-Layer Perceptrons (MLPs) are first investigated. Subsequently, a novel design at nanoscale that combines parallelization and serialization techniques (so hybrid), is proposed to implement a high performance MLP. This design utilizes single precision floating-point arithmetic units and a fully pipelined scheme; it is very flexible because the hardware is independent of the network topology, except for the number of cycles and therefore, the latency. Simulation results for the MNIST dataset show that compared to existing MLP designs found in the technical literature, the proposed design achieves the least power consumption, the highest operating frequency and accuracy; however, it suffers from a slight increase in latency.

Nanoscale Design of Multi-Layer Perceptrons using Floating-Point Arithmetic Units

On a Multiprocessor System with Dynamic Redundancy.

The slowdown of CMOS technology scaling has placed architectures and algorithms on focus for future performance improvements in nanoscale computing systems. Two promising approaches at algorithmic level are approximate computing (AC) and probabilistic data structures (PDSs) that employ the tolerance of an application to small deviations in the results for reducing the complexity of the hardware implementation. AC focuses on applications that process numerical data and relies mostly on approximate (or inexact) low-level arithmetic operations. Instead, PDSs target categorical data and rely on shared data structures and other higher-level simplifications that introduce probabilistic deviations even when all operations are exact. Both AC and PDSs have been able to dramatically reduce the cost in some applications, but they are so far completely disconnected in the application domains, the abstraction levels, and the research communities. In this article, we introduce probabilistic approximate computing (PAC), a new paradigm to use application tolerance for small deviations to reduce the implementation complexity of data structures and hardware when implemented with nanoscale memory technologies. Its goal is to have data structures on which both AC and probabilistic techniques are used in a synergetic way to improve efficiency, while keeping deviations within acceptable margins.

Fabrizio Lombardi

Papers

Modeling intermediate tests for fault-tolerant multichip module systems

Fault detection in a tristate system environment

Nanoscale Design of Multi-Layer Perceptrons using Floating-Point Arithmetic Units

On a Multiprocessor System with Dynamic Redundancy.

Probabilistic Approximate Computing at Nanoscales: From data structures to memories