Plug & Test at System Level via Testable TLM Primitives

With the evolution of Electronic System Level (ESL) design methodologies, we are experiencing an extensive use of Transaction-Level Modeling (TLM). TLM is a high-level approach to modeling digital systems where details of the communication among modules are separated from the those of the implementation of functional units. This paper represents a first step toward the automatic insertion of testing capabilities at the transaction level by definition of testable TLM primitives. The use of testable TLM primitives should help designers to easily get testable transaction level descriptions implementing what we call a "Plug & Test" design methodology. The proposed approach is intended to work both with hardware and software implementations. In particular, in this paper we will focus on the design of a testable FIFO communication channel to show how designers are given the freedom of trading-off complexity, testability levels, and cos

Autonomous Recovery Of Reconfigurable Logic Devices Using Priority Escalation Of Slack

Field Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases. To extend these concepts to semiconductor aging and process variation in the deep submicron era, resilient adaptable processing systems are sought to maintain quality and throughput requirements despite the vulnerabilities of the underlying computational devices. A new approach to autonomous fault-handling which addresses these goals is developed using only a uniplex hardware arrangement. It operates by observing a health metric to achieve Fault Demotion using Recon- figurable Slack (FaDReS). Here an autonomous fault isolation scheme is employed which neither requires test vectors nor suspends the computational throughput, but instead observes the value of a health metric based on runtime input. The deterministic flow of the fault isolation scheme guarantees success in a bounded number of reconfigurations of the FPGA fabric. FaDReS is then extended to the Priority Using Resource Escalation (PURE) online redundancy scheme which considers fault-isolation latency and throughput trade-offs under a dynamic spare arrangement. While deep-submicron designs introduce new challenges, use of adaptive techniques are seen to provide several promising avenues for improving resilience. The scheme developed is demonstrated by hardware design of various signal processing circuits and their implementation on a Xilinx Virtex-4 FPGA device. These include a Discrete Cosine Transform (DCT) core, Motion Estimation (ME) engine, Finite Impulse Response (FIR) Filter, Support Vector Machine (SVM), and Advanced Encryption Standard (AES) blocks in addition to MCNC benchmark circuits. A iii significant reduction in power consumption is achieved ranging from 83% for low motion-activity scenes to 12.5% for high motion activity video scenes in a novel ME engine configuration. For a typical benchmark video sequence, PURE is shown to maintain a PSNR baseline near 32dB. The diagnosability, reconfiguration latency, and resource overhead of each approach is analyzed. Compared to previous alternatives, PURE maintains a PSNR within a difference of 4.02dB to 6.67dB from the fault-free baseline by escalating healthy resources to higher-priority signal processing functions. The results indicate the benefits of priority-aware resiliency over conventional redundancy approaches in terms of fault-recovery, power consumption, and resource-area requirements. Together, these provide a broad range of strategies to achieve autonomous recovery of reconfigurable logic devices under a variety of constraints, operating conditions, and optimization criteria

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Innovative Techniques for Testing and Diagnosing SoCs

We rely upon the continued functioning of many electronic devices for our everyday welfare, usually embedding integrated circuits that are becoming even cheaper and smaller with improved features. Nowadays, microelectronics can integrate a working computer with CPU, memories, and even GPUs on a single die, namely System-On-Chip (SoC). SoCs are also employed on automotive safety-critical applications, but need to be tested thoroughly to comply with reliability standards, in particular the ISO26262 functional safety for road vehicles. The goal of this PhD. thesis is to improve SoC reliability by proposing innovative techniques for testing and diagnosing its internal modules: CPUs, memories, peripherals, and GPUs. The proposed approaches in the sequence appearing in this thesis are described as follows: 1. Embedded Memory Diagnosis: Memories are dense and complex circuits which are susceptible to design and manufacturing errors. Hence, it is important to understand the fault occurrence in the memory array. In practice, the logical and physical array representation differs due to an optimized design which adds enhancements to the device, namely scrambling. This part proposes an accurate memory diagnosis by showing the efforts of a software tool able to analyze test results, unscramble the memory array, map failing syndromes to cell locations, elaborate cumulative analysis, and elaborate a final fault model hypothesis. Several SRAM memory failing syndromes were analyzed as case studies gathered on an industrial automotive 32-bit SoC developed by STMicroelectronics. The tool displayed defects virtually, and results were confirmed by real photos taken from a microscope. 2. Functional Test Pattern Generation: The key for a successful test is the pattern applied to the device. They can be structural or functional; the former usually benefits from embedded test modules targeting manufacturing errors and is only effective before shipping the component to the client. The latter, on the other hand, can be applied during mission minimally impacting on performance but is penalized due to high generation time. However, functional test patterns may benefit for having different goals in functional mission mode. Part III of this PhD thesis proposes three different functional test pattern generation methods for CPU cores embedded in SoCs, targeting different test purposes, described as follows: a. Functional Stress Patterns: Are suitable for optimizing functional stress during I Operational-life Tests and Burn-in Screening for an optimal device reliability characterization b. Functional Power Hungry Patterns: Are suitable for determining functional peak power for strictly limiting the power of structural patterns during manufacturing tests, thus reducing premature device over-kill while delivering high test coverage c. Software-Based Self-Test Patterns: Combines the potentiality of structural patterns with functional ones, allowing its execution periodically during mission. In addition, an external hardware communicating with a devised SBST was proposed. It helps increasing in 3% the fault coverage by testing critical Hardly Functionally Testable Faults not covered by conventional SBST patterns. An automatic functional test pattern generation exploiting an evolutionary algorithm maximizing metrics related to stress, power, and fault coverage was employed in the above-mentioned approaches to quickly generate the desired patterns. The approaches were evaluated on two industrial cases developed by STMicroelectronics; 8051-based and a 32-bit Power Architecture SoCs. Results show that generation time was reduced upto 75% in comparison to older methodologies while increasing significantly the desired metrics. 3. Fault Injection in GPGPU: Fault injection mechanisms in semiconductor devices are suitable for generating structural patterns, testing and activating mitigation techniques, and validating robust hardware and software applications. GPGPUs are known for fast parallel computation used in high performance computing and advanced driver assistance where reliability is the key point. Moreover, GPGPU manufacturers do not provide design description code due to content secrecy. Therefore, commercial fault injectors using the GPGPU model is unfeasible, making radiation tests the only resource available, but are costly. In the last part of this thesis, we propose a software implemented fault injector able to inject bit-flip in memory elements of a real GPGPU. It exploits a software debugger tool and combines the C-CUDA grammar to wisely determine fault spots and apply bit-flip operations in program variables. The goal is to validate robust parallel algorithms by studying fault propagation or activating redundancy mechanisms they possibly embed. The effectiveness of the tool was evaluated on two robust applications: redundant parallel matrix multiplication and floating point Fast Fourier Transform

Virtual Runtime Application Partitions for Resource Management in Massively Parallel Architectures

This thesis presents a novel design paradigm, called Virtual Runtime Application Partitions (VRAP), to judiciously utilize the on-chip resources. As the dark silicon era approaches, where the power considerations will allow only a fraction chip to be powered on, judicious resource management will become a key consideration in future designs. Most of the works on resource management treat only the physical components (i.e. computation, communication, and memory blocks) as resources and manipulate the component to application mapping to optimize various parameters (e.g. energy efficiency). To further enhance the optimization potential, in addition to the physical resources we propose to manipulate abstract resources (i.e. voltage/frequency operating point, the fault-tolerance strength, the degree of parallelism, and the configuration architecture). The proposed framework (i.e. VRAP) encapsulates methods, algorithms, and hardware blocks to provide each application with the abstract resources tailored to its needs. To test the efficacy of this concept, we have developed three distinct self adaptive environments: (i) Private Operating Environment (POE), (ii) Private Reliability Environment (PRE), and (iii) Private Configuration Environment (PCE) that collectively ensure that each application meets its deadlines using minimal platform resources. In this work several novel architectural enhancements, algorithms and policies are presented to realize the virtual runtime application partitions efficiently. Considering the future design trends, we have chosen Coarse Grained Reconfigurable Architectures (CGRAs) and Network on Chips (NoCs) to test the feasibility of our approach. Specifically, we have chosen Dynamically Reconfigurable Resource Array (DRRA) and McNoC as the representative CGRA and NoC platforms. The proposed techniques are compared and evaluated using a variety of quantitative experiments. Synthesis and simulation results demonstrate VRAP significantly enhances the energy and power efficiency compared to state of the art.Siirretty Doriast

Adaptive Distributed Architectures for Future Semiconductor Technologies.

Year after year semiconductor manufacturing has been able to integrate more components in a single computer chip. These improvements have been possible through systematic shrinking in the size of its basic computational element, the transistor. This trend has allowed computers to progressively become faster, more efficient and less expensive. As this trend continues, experts foresee that current computer designs will face new challenges, in utilizing the minuscule devices made available by future semiconductor technologies. Today's microprocessor designs are not fit to overcome these challenges, since they are constrained by their inability to handle component failures by their lack of adaptability to a wide range of custom modules optimized for specific applications and by their limited design modularity. The focus of this thesis is to develop original computer architectures, that can not only survive these new challenges, but also leverage the vast number of transistors available to unlock better performance and efficiency. The work explores and evaluates new software and hardware techniques to enable the development of novel adaptive and modular computer designs. The thesis first explores an infrastructure to quantitatively assess the fallacies of current systems and their inadequacy to operate on unreliable silicon. In light of these findings, specific solutions are then proposed to strengthen digital system architectures, both through hardware and software techniques. The thesis culminates with the proposal of a radically new architecture design that can fully adapt dynamically to operate on the hardware resources available on chip, however limited or abundant those may be.PHDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102405/1/apellegr_1.pd

Dependable design for low-cost ultra-low-power processors

Emerging applications in the Internet of Things (IoT) domain, such as wearables, implantables, smart tags, and wireless sensor networks put severe power, cost, reliability, and security constraints on hardware system design. This dissertation focuses on the architecture and design of dependable ultra-low power computing systems. Specifically, it proposes architecture and design techniques that exploit the unique application and usage characteristics of future computing systems to deliver low power, while meeting the reliability and security constraints of these systems. First, this dissertation considers the challenge of achieving both low power and high reliability in SRAM memories. It proposes both an architectural technique to reduce the overheads of error correction and a technique that uses the nature of error correcting codes to allow lower voltage operation without sacrificing reliability. Next, this dissertation considers low power and low cost. By leveraging the fact that many IoT systems are embedded in nature and will run the same application for their entire lifetime, fine-grained usage characteristics of the hardware-software system can be determined at design time. This dissertation presents a novel hardware-software co-analysis based on symbolic simulation that can determine the possible states of the processor throughout any execution of a specific application. This enables power-gating where more gates are turned off for longer, bespoke processors customized to specific applications, and stricter determination of peak power bounds. Finally, this dissertation considers achieving secure IoT systems at low cost and power overhead. By leveraging the hardware-software co-analysis, this dissertation shows that gate-level information flow security guarantees can be provided without hardware overheads

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Low-cost and efficient fault detection and diagnosis schemes for modern cores

Continuous improvements in transistor scaling together with microarchitectural advances have made possible the widespread adoption of high-performance processors across all market segments. However, the growing reliability threats induced by technology scaling and by the complexity of designs are challenging the production of cheap yet robust systems. Soft error trends are haunting, especially for combinational logic, and parity and ECC codes are therefore becoming insufficient as combinational logic turns into the dominant source of soft errors. Furthermore, experts are warning about the need to also address intermittent and permanent faults during processor runtime, as increasing temperatures and device variations will accelerate inherent aging phenomena. These challenges specially threaten the commodity segments, which impose requirements that existing fault tolerance mechanisms cannot offer. Current techniques based on redundant execution were devised in a time when high penalties were assumed for the sake of high reliability levels. Novel light-weight techniques are therefore needed to enable fault protection in the mass market segments. The complexity of designs is making post-silicon validation extremely expensive. Validation costs exceed design costs, and the number of discovered bugs is growing, both during validation and once products hit the market. Fault localization and diagnosis are the biggest bottlenecks, magnified by huge detection latencies, limited internal observability, and costly server farms to generate test outputs. This thesis explores two directions to address some of the critical challenges introduced by unreliable technologies and by the limitations of current validation approaches. We first explore mechanisms for comprehensively detecting multiple sources of failures in modern processors during their lifetime (including transient, intermittent, permanent and also design bugs). Our solutions embrace a paradigm where fault tolerance is built based on exploiting high-level microarchitectural invariants that are reusable across designs, rather than relying on re-execution or ad-hoc block-level protection. To do so, we decompose the basic functionalities of processors into high-level tasks and propose three novel runtime verification solutions that combined enable global error detection: a computation/register dataflow checker, a memory dataflow checker, and a control flow checker. The techniques use the concept of end-to-end signatures and allow designers to adjust the fault coverage to their needs, by trading-off area, power and performance. Our fault injection studies reveal that our methods provide high coverage levels while causing significantly lower performance, power and area costs than existing techniques. Then, this thesis extends the applicability of the proposed error detection schemes to the validation phases. We present a fault localization and diagnosis solution for the memory dataflow by combining our error detection mechanism, a new low-cost logging mechanism and a diagnosis program. Selected internal activity is continuously traced and kept in a memory-resident log whose capacity can be expanded to suite validation needs. The solution can catch undiscovered bugs, reducing the dependence on simulation farms that compute golden outputs. Upon error detection, the diagnosis algorithm analyzes the log to automatically locate the bug, and also to determine its root cause. Our evaluations show that very high localization coverage and diagnosis accuracy can be obtained at very low performance and area costs. The net result is a simplification of current debugging practices, which are extremely manual, time consuming and cumbersome. Altogether, the integrated solutions proposed in this thesis capacitate the industry to deliver more reliable and correct processors as technology evolves into more complex designs and more vulnerable transistors.El continuo escalado de los transistores junto con los avances microarquitectónicos han posibilitado la presencia de potentes procesadores en todos los segmentos de mercado. Sin embargo, varios problemas de fiabilidad están desafiando la producción de sistemas robustos. Las predicciones de "soft errors" son inquietantes, especialmente para la lógica combinacional: soluciones como ECC o paridad se están volviendo insuficientes a medida que dicha lógica se convierte en la fuente predominante de soft errors. Además, los expertos están alertando acerca de la necesidad de detectar otras fuentes de fallos (causantes de errores permanentes e intermitentes) durante el tiempo de vida de los procesadores. Los segmentos "commodity" son los más vulnerables, ya que imponen unos requisitos que las técnicas actuales de fiabilidad no ofrecen. Estas soluciones (generalmente basadas en re-ejecución) fueron ideadas en un tiempo en el que con tal de alcanzar altos nivel de fiabilidad se asumían grandes costes. Son por tanto necesarias nuevas técnicas que permitan la protección contra fallos en los segmentos más populares. La complejidad de los diseños está encareciendo la validación "post-silicon". Su coste excede el de diseño, y el número de errores descubiertos está aumentando durante la validación y ya en manos de los clientes. La localización y el diagnóstico de errores son los mayores problemas, empeorados por las altas latencias en la manifestación de errores, por la poca observabilidad interna y por el coste de generar las señales esperadas. Esta tesis explora dos direcciones para tratar algunos de los retos causados por la creciente vulnerabilidad hardware y por las limitaciones de los enfoques de validación. Primero exploramos mecanismos para detectar múltiples fuentes de fallos durante el tiempo de vida de los procesadores (errores transitorios, intermitentes, permanentes y de diseño). Nuestras soluciones son de un paradigma donde la fiabilidad se construye explotando invariantes microarquitectónicos genéricos, en lugar de basarse en re-ejecución o en protección ad-hoc. Para ello descomponemos las funcionalidades básicas de un procesador y proponemos tres soluciones de `runtime verification' que combinadas permiten una detección de errores a nivel global. Estas tres soluciones son: un verificador de flujo de datos de registro y de computación, un verificador de flujo de datos de memoria y un verificador de flujo de control. Nuestras técnicas usan el concepto de firmas y permiten a los diseñadores ajustar los niveles de protección a sus necesidades, mediante compensaciones en área, consumo energético y rendimiento. Nuestros estudios de inyección de errores revelan que los métodos propuestos obtienen altos niveles de protección, a la vez que causan menos costes que las soluciones existentes. A continuación, esta tesis explora la aplicabilidad de estos esquemas a las fases de validación. Proponemos una solución de localización y diagnóstico de errores para el flujo de datos de memoria que combina nuestro mecanismo de detección de errores, junto con un mecanismo de logging de bajo coste y un programa de diagnóstico. Cierta actividad interna es continuamente registrada en una zona de memoria cuya capacidad puede ser expandida para satisfacer las necesidades de validación. La solución permite descubrir bugs, reduciendo la necesidad de calcular los resultados esperados. Al detectar un error, el algoritmo de diagnóstico analiza el registro para automáticamente localizar el bug y determinar su causa. Nuestros estudios muestran un alto grado de localización y de precisión de diagnóstico a un coste muy bajo de rendimiento y área. El resultado es una simplificación de las prácticas actuales de depuración, que son enormemente manuales, incómodas y largas. En conjunto, las soluciones de esta tesis capacitan a la industria a producir procesadores más fiables, a medida que la tecnología evoluciona hacia diseños más complejos y más vulnerables