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

Real-time electrocardiogram (ECG) monitoring using wearable devices is crucial for early cardiovascular disease diagnosis and by using machine learning (ML) algorithms, it can be automated. Unfortunately, wearable devices face stringent hardware resource constraints, and thus low-complexity designs that can implement ML-based detection of heartbeat anomalies are required. This paper proposes the integration of a delta modulator (DM) used to digitize the ECG signal with a Stochastic Computing (SC) implementation of the ML algorithms. The DM enables a low-cost conversion of the ECG to binary sequences that are then directly processed in the SC implementation of an ML algorithm. This eliminates the need of converting the DM outputs to integers and then to stochastic sequences and thus the proposed integrated design considerably reduces the complexity of the system. The proposed scheme has been evaluated on a premature ventricular contraction (PVC) heartbeat recognition system based on a support vector machine classifier. The estimated chip area and power dissipation of the proposed system using a commercial 180nm CMOS technology are 0.36 mm2 and 0.6 µW, respectively, so achieving more than 38% and 54% reduction in these metrics compared to state-of-the-art solutions while providing similar performance in terms of heartbeat anomaly detection.

Integrating Delta Modulation and Stochastic Computing for Real-time Machine Learning based Heartbeats Monitoring in Wearable Systems

This paper analyzes several delay estimates for metallic carbon nanotubes (CNT) as interconnects of very large scale integrated (VLSI) chips. A study of the 2005 edition of the international technology roadmap (ITRS) [1] for global/intermediate interconnects is presented to highlight the significant issues encountered with the projected performance of copper/aluminum interconnects till 2020. Then a worst case performance analysis of metallic CNTs is presented and compared versus copper interconnects. It is evaluated that the RC delay of CNTs does not meet the future RC delay requirements of on-chip intermediate and global wires.

A performance analysis for single-walled metallic Carbon Nanotubes as global and intermediate on-chip interconnects

The problem of establishing reliable communication across point-to-point networks is addressed. Several protocols are given, using node-disjoint paths to implement a set of independent virtual links (u-links) between each node pair. The u-links can be used to establish communication between the node pair provided the number and type of failures in the network are limited. Failed devices are classified by whether they are benign (delay and omission faults) or are malicious (not benign). The use of u-links is demonstrated with a protocol for remote information retrieval. With b benign and m malicious faults, retrieval can always be achieved if the graph connectivity exceeds b+2 m. This bound is tight. The authors give a flexible retrieval protocol; it analyzes the responses received thus far, and computes a minimum and maximum number of u-links to send (additional) retrieval requests. Maximal requests guarantee completion of the protocol upon their timeout. With minimal requests, protocol completion is barely possible with no further requests. u-links are extended so that they bounce requests when there is no response, which strengthens the retrieval protocol.

Using virtual links for reliable information retrieval across point-to-point networks

The separation of memory and computing units in the von Neumann architecture leads to undesirable energy consumption due to data movement and insufficient memory bandwidth. Energy-efficient in-memory computing platforms have the potential to address such issues. Due to its non-volatility and advantageous features over CMOS (such as low power, near-zero leakage current and high integration density), spin-based devices have been advocated for in-memory computing. This paper proposes a field-free Spin Orbit Torque (FF-SOT) MRAM based computing-in-memory (CiM) scheme that realizes XNOR/XOR logic and a cascading adder. This novel FF-SOT-CiM design does not require expensive peripheral circuits for computation while using the same memory cell design as a SOT-MRAM. Furthermore, FF-SOT-CiM does not require additional write cycles to save the result of its computations in the memory. The design offers higher write speed and; lower operating energy compared to CiM schemes based on other technologies; it also alleviates the source degeneration effect by leveraging an advanced switching mechanism. Extensive simulation results show that the proposed FF-SOT-CiM achieves up to 3.1x (2.6x) latency (energy) reduction compared to SRAM-based CiM, with negligible hardware overhead when performing in-memory XOR. ADD operations; the proposed FF-SOT-CiM can be to 5.0X and 1.5X faster and 3.4X and 1.1X more energy efficient than existing STT-based and FeFET-based schemes, respectively.

Fabrizio Lombardi

Papers

Integrating Delta Modulation and Stochastic Computing for Real-time Machine Learning based Heartbeats Monitoring in Wearable Systems

A performance analysis for single-walled metallic Carbon Nanotubes as global and intermediate on-chip interconnects

Using virtual links for reliable information retrieval across point-to-point networks

An Energy-Efficient Computing-in-Memory (CiM) Scheme Using Field-Free Spin-Orbit Torque (SOT) Magnetic RAMs

A low complexity approach for fault detection in C-testable orthogonal VLSI arrays