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The objective of this research is to optimize the testing time and test resource allocation for System-on-a-Chip (SOC). The mathematical formulation and the neural networks with different techniques are proposed to solve these SOC test problems. First, a fixed-weight neural network combined with heuristic algorithms has been developed to solve the SOC test scheduling problems. The objective of this SOC test automation is to minimize the SOC testing time subject to different constraints: (i) precedence constraint, (ii) resource constraint, (iii) core constraint, and (iv) power constraint. Heuristic algorithms are often used to prevent the neural network from getting trapped in a local optima. The developed neural network can effectively solve the SOC test scheduling models with disjunctive constraints. The results show that the proposed method can efficiently solve a large-size SOC test scheduling problem within reasonable computing time. Second, to solve the resource allocation and the width selection problems for SOC test automation, a maximum neural network (MNN) has been proposed in this research for handling more complex SOC test problems. The SOC test automation problem with resource allocation is a NP-hard problem. The proposed maximum neural network can be used to solve the NP-hard SOC test problems within polynomial time. The results show that, by using the developed maximum neural network, the overall testing time for the SOC can be minimized with optimal resource allocation and test access mechanism (TAM) width selection. The computation time of the proposed method is significantly less than the time for traditional methods such as the integer linear programming (ILP) or heuristic algorithms. Third, the SOC test automation problems with core test wrapper design have been studied in this research. The core test wrapper design provides an interface between the core and the SOC in which the core is embedded. After the core test wrapper is designed, the total.
Keywords: System-on-a-Chip, neural networks, test scheduling, embedded core testing, semiconductor manufacturing.
System-on-a-Chip (SOC) integrated circuits composed of embedded cores are now commonplace. Nevertheless, there remain several roadblocks to rapid and efficient system integration. Test development is seen as a major bottleneck in SOC design and manufacturing capabilities. Testing SOCs is especially challenging in the absence of standardized test structures, test automation tools, and test protocols. In addition, long interconnects, high density, and high-speed designs lead to new types of faults involving crosstalk and signal integrity. SOC (System-on-a-Chip) Testing for Plug and Play Test Automation is an edited work containing thirteen contributions that address various aspects of SOC testing. SOC (System-on-a-Chip) Testing for Plug and Play Test Automation is a valuable reference for researchers and students interested in various aspects of SOC testing.
SOC test design and its optimization is the topic of Introduction to Advanced System-on-Chip Test Design and Optimization. It gives an introduction to testing, describes the problems related to SOC testing, discusses the modeling granularity and the implementation into EDA (electronic design automation) tools. The book is divided into three sections: i) test concepts, ii) SOC design for test, and iii) SOC test applications. The first part covers an introduction into test problems including faults, fault types, design-flow, design-for-test techniques such as scan-testing and Boundary Scan. The second part of the book discusses SOC related problems such as system modeling, test conflicts, power consumption, test access mechanism design, test scheduling and defect-oriented scheduling. Finally, the third part focuses on SOC applications, such as integrated test scheduling and TAM design, defect-oriented scheduling, and integrating test design with the core selection process.
Test Resource Partitioning for System-on-a-Chip is about test resource partitioning and optimization techniques for plug-and-play system-on-a-chip (SOC) test automation. Plug-and-play refers to the paradigm in which core-to-core interfaces as well as core-to-SOC logic interfaces are standardized, such that cores can be easily plugged into "virtual sockets" on the SOC design, and core tests can be plugged into the SOC during test without substantial effort on the part of the system integrator. The goal of the book is to position test resource partitioning in the context of SOC test automation, as well as to generate interest and motivate research on this important topic. SOC integrated circuits composed of embedded cores are now commonplace. Nevertheless, There remain several roadblocks to rapid and efficient system integration. Test development is seen as a major bottleneck in SOC design, and test challenges are a major contributor to the widening gap between design capability and manufacturing capacity. Testing SOCs is especially challenging in the absence of standardized test structures, test automation tools, and test protocols. Test Resource Partitioning for System-on-a-Chip responds to a pressing need for a structured methodology for SOC test automation. It presents new techniques for the partitioning and optimization of the three major SOC test resources: test hardware, testing time and test data volume. Test Resource Partitioning for System-on-a-Chip paves the way for a powerful integrated framework to automate the test flow for a large number of cores in an SOC in a plug-and-play fashion. The framework presented allows the system integrator to reduce test cost and meet short time-to-market requirements.
This book originated from a workshop held at the DATE 2005 conference, namely Designing Complex SOCs. State-of-the-art in issues related to System-on-Chip (SoC) design by leading experts in the fields, it covers IP development, verification, integration, chip implementation, testing and software. It contains valuable academic and industrial examples for those involved with the design of complex SOCs.
SOC test design and its optimization is the topic of Introduction to Advanced System-on-Chip Test Design and Optimization. It gives an introduction to testing, describes the problems related to SOC testing, discusses the modeling granularity and the implementation into EDA (electronic design automation) tools. The book is divided into three sections: i) test concepts, ii) SOC design for test, and iii) SOC test applications. The first part covers an introduction into test problems including faults, fault types, design-flow, design-for-test techniques such as scan-testing and Boundary Scan. The second part of the book discusses SOC related problems such as system modeling, test conflicts, power consumption, test access mechanism design, test scheduling and defect-oriented scheduling. Finally, the third part focuses on SOC applications, such as integrated test scheduling and TAM design, defect-oriented scheduling, and integrating test design with the core selection process.
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size