Automating defects simulation and fault modeling for SRAMs

The continues improvement in manufacturing process density for very deep sub micron technologies constantly leads to new classes of defects in memory devices. Exploring the effect of fabrication defects in future technologies, and identifying new classes of realistic functional fault models with their corresponding test sequences, is a time consuming task up to now mainly performed by hand. This paper proposes a new approach to automate this procedure. The proposed method exploits the capabilities of evolutionary algorithms to automatically identify faulty behaviors into defective memories and to define the corresponding fault models and relevant test sequences. Target defects are modeled at the electrical level in order to optimize the results to the specific technology and memory architecture

Multi-core devices for safety-critical systems: a survey

Multi-core devices are envisioned to support the development of next-generation safety-critical systems, enabling the on-chip integration of functions of different criticality. This integration provides multiple system-level potential benefits such as cost, size, power, and weight reduction. However, safety certification becomes a challenge and several fundamental safety technical requirements must be addressed, such as temporal and spatial independence, reliability, and diagnostic coverage. This survey provides a categorization and overview at different device abstraction levels (nanoscale, component, and device) of selected key research contributions that support the compliance with these fundamental safety requirements.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-65316-P, Basque Government under grant KK-2019-00035 and the HiPEAC Network of Excellence. The Spanish Ministry of Economy and Competitiveness has also partially supported Jaume Abella under Ramon y Cajal postdoctoral fellowship (RYC-2013-14717).Peer ReviewedPostprint (author's final draft

GPU devices for safety-critical systems: a survey

Graphics Processing Unit (GPU) devices and their associated software programming languages and frameworks can deliver the computing performance required to facilitate the development of next-generation high-performance safety-critical systems such as autonomous driving systems. However, the integration of complex, parallel, and computationally demanding software functions with different safety-criticality levels on GPU devices with shared hardware resources contributes to several safety certification challenges. This survey categorizes and provides an overview of research contributions that address GPU devices’ random hardware failures, systematic failures, and independence of execution.This work has been partially supported by the European Research Council with Horizon 2020 (grant agreements No. 772773 and 871465), the Spanish Ministry of Science and Innovation under grant PID2019-107255GB, the HiPEAC Network of Excellence and the Basque Government under grant KK-2019-00035. The Spanish Ministry of Economy and Competitiveness has also partially supported Leonidas Kosmidis with a Juan de la Cierva Incorporación postdoctoral fellowship (FJCI-2020- 045931-I).Peer ReviewedPostprint (author's final draft

DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints

Fault Detection Methodology for Caches in Reliable Modern VLSI Microprocessors based on Instruction Set Architectures

Η παρούσα διδακτορική διατριβή εισάγει μία χαμηλού κόστους μεθοδολογία για την ανίχνευση ελαττωμάτων σε μικρές ενσωματωμένες κρυφές μνήμες που βασίζεται σε σύγχρονες Αρχιτεκτονικές Συνόλου Εντολών και εφαρμόζεται με λογισμικό αυτοδοκιμής. Η προτεινόμενη μεθοδολογία εφαρμόζει αλγορίθμους March μέσω λογισμικού για την ανίχνευση τόσο ελαττωμάτων αποθήκευσης όταν εφαρμόζεται σε κρυφές μνήμες που περιέχουν μόνο στατικές μνήμες τυχαίας προσπέλασης όπως για παράδειγμα κρυφές μνήμες επιπέδου 1, όσο και ελαττωμάτων σύγκρισης όταν εφαρμόζεται σε κρυφές μνήμες που περιέχουν εκτός από SRAM μνήμες και μνήμες διευθυνσιοδοτούμενες μέσω περιεχομένου, όπως για παράδειγμα πλήρως συσχετιστικές κρυφές μνήμες αναζήτησης μετάφρασης. Η προτεινόμενη μεθοδολογία εφαρμόζεται και στις τρεις οργανώσεις συσχετιστικότητας κρυφής μνήμης και είναι ανεξάρτητη της πολιτικής εγγραφής στο επόμενο επίπεδο της ιεραρχίας. Η μεθοδολογία αξιοποιεί υπάρχοντες ισχυρούς μηχανισμούς των μοντέρνων ISAs χρησιμοποιώντας ειδικές εντολές, που ονομάζονται στην παρούσα διατριβή Εντολές Άμεσης Προσπέλασης Κρυφής Μνήμης (Direct Cache Access Instructions - DCAs). Επιπλέον, η προτεινόμενη μεθοδολογία εκμεταλλεύεται τους έμφυτους μηχανισμούς καταγραφής απόδοσης και τους μηχανισμούς χειρισμού παγίδων που είναι διαθέσιμοι στους σύγχρονους επεξεργαστές. Επιπρόσθετα, η προτεινόμενη μεθοδολογία εφαρμόζει την λειτουργία σύγκρισης των αλγορίθμων March όταν αυτή απαιτείται (για μνήμες CAM) και επαληθεύει το αποτέλεσμα του ελέγχου μέσω σύντομης απόκρισης, ώστε να είναι συμβατή με τις απαιτήσεις του ελέγχου εντός λειτουργίας. Τέλος, στη διατριβή προτείνεται μία βελτιστοποίηση της μεθοδολογίας για πολυνηματικές, πολυπύρηνες αρχιτεκτονικές.The present PhD thesis introduces a low cost fault detection methodology for small embedded cache memories that is based on modern Instruction Set Architectures and is applied with Software-Based Self-Test (SBST) routines. The proposed methodology applies March tests through software to detect both storage faults when applied to caches that comprise Static Random Access Memories (SRAM) only, e.g. L1 caches, and comparison faults when applied to caches that apart from SRAM memories comprise Content Addressable Memories (CAM) too, e.g. Translation Lookaside Buffers (TLBs). The proposed methodology can be applied to all three cache associativity organizations: direct mapped, set-associative and full-associative and it does not depend on the cache write policy. The methodology leverages existing powerful mechanisms of modern ISAs by utilizing instructions that we call in this PhD thesis Direct Cache Access (DCA) instructions. Moreover, our methodology exploits the native performance monitoring hardware and the trap handling mechanisms which are available in modern microprocessors. Moreover, the proposed Methodology applies March compare operations when needed (for CAM arrays) and verifies the test result with a compact response to comply with periodic on-line testing needs. Finally, a multithreaded optimization of the proposed methodology that targets multithreaded, multicore architectures is also presented in this thesi

Automotive embedded systems software reprogramming

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThe exponential growth of computer power is no longer limited to stand alone computing systems but applies to all areas of commercial embedded computing systems. The ongoing rapid growth in intelligent embedded systems is visible in the commercial automotive area, where a modern car today implements up to 80 different electronic control units (ECUs) and their total memory size has been increased to several hundreds of megabyte. This growth in the commercial mass production world has led to new challenges, even within the automotive industry but also in other business areas where cost pressure is high. The need to drive cost down means that every cent spent on recurring engineering costs needs to be justified. A conflict between functional requirements (functionality, system reliability, production and manufacturing aspects etc.), testing and maintainability aspects is given. Software reprogramming, as a key issue within the automotive industry, solve that given conflict partly in the past. Software Reprogramming for in-field service and maintenance in the after sales markets provides a strong method to fix previously not identified software errors. But the increasing software sizes and therefore the increasing software reprogramming times will reduce the benefits. Especially if ECU’s software size growth faster than vehicle’s onboard infrastructure can be adjusted. The thesis result enables cost prediction of embedded systems’ software reprogramming by generating an effective and reliable model for reprogramming time for different existing and new technologies. This model and additional research results contribute to a timeline for short term, mid term and long term solutions which will solve the currently given problems as well as future challenges, especially for the automotive industry but also for all other business areas where cost pressure is high and software reprogramming is a key issue during products life cycle

Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications

On-Line Dependability Enhancement of Multiprocessor SoCs by Resource Management

This paper describes a new approach towards dependable design of homogeneous multi-processor SoCs in an example satellite-navigation application. First, the NoC dependability is functionally verified via embedded software. Then the Xentium processor tiles are periodically verified via on-line self-testing techniques, by using a new IIP Dependability Manager. Based on the Dependability Manager results, faulty tiles are electronically excluded and replaced by fault-free spare tiles via on-line resource management. This integrated approach enables fast electronic fault detection/diagnosis and repair, and hence a high system availability. The dependability application runs in parallel with the actual application, resulting in a very dependable system. All parts have been verified by simulation

Fault-tolerant computer study

A set of building block circuits is described which can be used with commercially available microprocessors and memories to implement fault tolerant distributed computer systems. Each building block circuit is intended for VLSI implementation as a single chip. Several building blocks and associated processor and memory chips form a self checking computer module with self contained input output and interfaces to redundant communications buses. Fault tolerance is achieved by connecting self checking computer modules into a redundant network in which backup buses and computer modules are provided to circumvent failures. The requirements and design methodology which led to the definition of the building block circuits are discussed