MISSED: an environment for mixed-signal microsystem testing and diagnosis

A tight link between design and test data is proposed for speeding up test-pattern generation and diagnosis during mixed-signal prototype verification. Test requirements are already incorporated at the behavioral level and specified with increased detail at lower hierarchical levels. A strict distinction between generic routines and implementation data makes reuse of software possible. A testability-analysis tool and test and DFT libraries support the designer to guarantee testability. Hierarchical backtrace procedures in combination with an expert system and fault libraries assist the designer during mixed-signal chip debuggin

A design for testability study on a high performance automatic gain control circuit.

A comprehensive testability study on a commercial automatic gain control circuit is presented which aims to identify design for testability (DfT) modifications to both reduce production test cost and improve test quality. A fault simulation strategy based on layout extracted faults has been used to support the study. The paper proposes a number of DfT modifications at the layout, schematic and system levels together with testability. Guidelines that may well have generic applicability. Proposals for using the modifications to achieve partial self test are made and estimates of achieved fault coverage and quality levels presente

MAGNETO-ELECTRIC APPROXIMATE COMPUTATIONAL FRAMEWORK FOR BAYESIAN INFERENCE

Probabilistic graphical models like Bayesian Networks (BNs) are powerful artificial-intelligence formalisms, with similarities to cognition and higher order reasoning in the human brain. These models have been, to great success, applied to several challenging real-world applications. Use of these formalisms to a greater set of applications is impeded by the limitations of the currently used software-based implementations. New emerging-technology based circuit paradigms which leverage physical equivalence, i.e., operating directly on probabilities vs. introducing layers of abstraction, promise orders of magnitude increase in performance and efficiency of BN implementations, enabling networks with millions of random variables. While majority of applications with small network size (100s of nodes) require only single digit precision for accurate results, applications with larger size (1000s to millions of nodes) require higher precision computation. We introduce a new BN integrated circuit fabric based on mixed-signal magneto-electric circuits which perform probabilistic computations based on the principle of approximate computation. Precision scaling in this fabric is logarithmic in area vs. linear in prior directions. Results show 33x area benefit for a 0.001 precision compared to prior direction, while maintaining three orders of magnitude performance benefits vs. 100-core processor implementations

A Holistic Approach to Functional Safety for Networked Cyber-Physical Systems

Functional safety is a significant concern in today's networked cyber-physical systems such as connected machines, autonomous vehicles, and intelligent environments. Simulation is a well-known methodology for the assessment of functional safety. Simulation models of networked cyber-physical systems are very heterogeneous relying on digital hardware, analog hardware, and network domains. Current functional safety assessment is mainly focused on digital hardware failures while minor attention is devoted to analog hardware and not at all to the interconnecting network. In this work we believe that in networked cyber-physical systems, the dependability must be verified not only for the nodes in isolation but also by taking into account their interaction through the communication channel. For this reason, this work proposes a holistic methodology for simulation-based safety assessment in which safety mechanisms are tested in a simulation environment reproducing the high-level behavior of digital hardware, analog hardware, and network communication. The methodology relies on three main automatic processes: 1) abstraction of analog models to transform them into system-level descriptions, 2) synthesis of network infrastructures to combine multiple cyber-physical systems, and 3) multi-domain fault injection in digital, analog, and network. Ultimately, the flow produces a homogeneous optimized description written in C++ for fast and reliable simulation which can have many applications. The focus of this thesis is performing extensive fault simulation and evaluating different functional safety metrics, \eg, fault and diagnostic coverage of all the safety mechanisms

The Galileo PPS expert monitoring and diagnostic prototype

The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text