Design and implementation of an out of order execution engine of floating point arithmetic operations

In this thesis, work is undertaken towards the design in hardware description languages and implementation in FPGA of an out of order execution engine of floating point arithmetic operations. This thesis work, is part of a project called Lagarto

Methods for Robust and Energy-Efficient Microprocessor Architectures

Σήμερα, η εξέλιξη της τεχνολογίας επιτρέπει τη βελτίωση τριών βασικών στοιχείων της σχεδίασης των επεξεργαστών: αυξημένες επιδόσεις, χαμηλότερη κατανάλωση ισχύος και χαμηλότερο κόστος παραγωγής του τσιπ, ενώ οι σχεδιαστές επεξεργαστών έχουν επικεντρωθεί στην παραγωγή επεξεργαστών με περισσότερες λειτουργίες σε χαμηλότερο κόστος. Οι σημερινοί επεξεργαστές είναι πολύ ταχύτεροι και διαθέτουν εξελιγμένες λειτουργικές μονάδες συγκριτικά με τους προκατόχους τους, ωστόσο, καταναλώνουν αρκετά μεγάλη ενέργεια. Τα ποσά ηλεκτρικής ισχύος που καταναλώνονται, και η επακόλουθη έκλυση θερμότητας, αυξάνονται παρά τη μείωση του μεγέθους των τρανζίστορ. Αναπτύσσοντας όλο και πιο εξελιγμένους μηχανισμούς και λειτουργικές μονάδες για την αύξηση της απόδοσης και βελτίωση της ενέργειας, σε συνδυασμό με τη μείωση του μεγέθους των τρανζίστορ, οι επεξεργαστές έχουν γίνει εξαιρετικά πολύπλοκα συστήματα, καθιστώντας τη διαδικασία της επικύρωσής τους σημαντική πρόκληση για τη βιομηχανία ολοκληρωμένων κυκλωμάτων. Συνεπώς, οι κατασκευαστές επεξεργαστών αφιερώνουν επιπλέον χρόνο, προϋπολογισμό και χώρο στο τσιπ για να διασφαλίσουν ότι οι επεξεργαστές θα λειτουργούν σωστά κατά τη διάθεσή τους στη αγορά. Για τους λόγους αυτούς, η εργασία αυτή παρουσιάζει νέες μεθόδους για την επιτάχυνση και τη βελτίωση της φάσης της επικύρωσης, καθώς και για τη βελτίωση της ενεργειακής απόδοσης των σύγχρονων επεξεργαστών. Στο πρώτο μέρος της διατριβής προτείνονται δύο διαφορετικές μέθοδοι για την επικύρωση του επεξεργαστή, οι οποίες συμβάλλουν στην επιτάχυνση αυτής της διαδικασίας και στην αποκάλυψη σπάνιων σφαλμάτων στους μηχανισμούς μετάφρασης διευθύνσεων των σύγχρονων επεξεργαστών. Και οι δύο μέθοδοι καθιστούν ευκολότερη την ανίχνευση και τη διάγνωση σφαλμάτων, και επιταχύνουν την ανίχνευση του σφάλματος κατά τη φάση της επικύρωσης. Στο δεύτερο μέρος της διατριβής παρουσιάζεται μια λεπτομερής μελέτη χαρακτηρισμού των περιθωρίων τάσης σε επίπεδο συστήματος σε δύο σύγχρονους ARMv8 επεξεργαστές. Η μελέτη του χαρακτηρισμού προσδιορίζει τα αυξημένα περιθώρια τάσης που έχουν προκαθοριστεί κατά τη διάρκεια κατασκευής του κάθε μεμονωμένου πυρήνα του επεξεργαστή και αναλύει τυχόν απρόβλεπτες συμπεριφορές που μπορεί να προκύψουν σε συνθήκες μειωμένης τάσης. Για την μελέτη και καταγραφή της συμπεριφοράς του συστήματος υπό συνθήκες μειωμένης τάσης, παρουσιάζεται επίσης σε αυτή τη διατριβή μια απλή και ενοποιημένη συνάρτηση: η συνάρτηση πυκνότητας-σοβαρότητας. Στη συνέχεια, παρουσιάζεται αναλυτικά η ανάπτυξη ειδικά σχεδιασμένων προγραμμάτων (micro-viruses) τα οποία υποβάλουν της θεμελιώδεις δομές του επεξεργαστή σε μεγάλο φορτίο εργασίας. Αυτά τα προγράμματα στοχεύουν στην γρήγορη αναγνώριση των ασφαλών περιθωρίων τάσης. Τέλος, πραγματοποιείται ο χαρακτηρισμός των περιθωρίων τάσης σε εκτελέσεις πολλαπλών πυρήνων, καθώς επίσης και σε διαφορετικές συχνότητες, και προτείνεται ένα πρόγραμμα το οποίο εκμεταλλεύεται όλες τις διαφορετικές πτυχές του προβλήματος της κατανάλωσης ενέργειας και παρέχει μεγάλη εξοικονόμηση ενέργειας διατηρώντας παράλληλα υψηλά επίπεδα απόδοσης. Αυτή η μελέτη έχει ως στόχο τον εντοπισμό και την ανάλυση της σχέσης μεταξύ ενέργειας και απόδοσης σε διαφορετικούς συνδυασμούς τάσης και συχνότητας, καθώς και σε διαφορετικό αριθμό νημάτων/διεργασιών που εκτελούνται στο σύστημα, αλλά και κατανομής των προγραμμάτων στους διαθέσιμους πυρήνες.Technology scaling has enabled improvements in the three major design optimization objectives: increased performance, lower power consumption, and lower die cost, while system design has focused on bringing more functionality into products at lower cost. While today's microprocessors, are much faster and much more versatile than their predecessors, they also consume much power. As operating frequency and integration density increase, the total chip power dissipation increases. This is evident from the fact that due to the demand for increased functionality on a single chip, more and more transistors are being packed on a single die and hence, the switching frequency increases in every technology generation. However, by developing aggressive and sophisticated mechanisms to boost performance and to enhance the energy efficiency in conjunction with the decrease of the size of transistors, microprocessors have become extremely complex systems, making the microprocessor verification and manufacturing testing a major challenge for the semiconductor industry. Manufacturers, therefore, choose to spend extra effort, time, budget and chip area to ensure that the delivered products are operating correctly. To meet high-dependability requirements, manufacturers apply a sequence of verification tasks throughout the entire life-cycle of the microprocessor to ensure the correct functionality of the microprocessor chips from the various types of errors that may occur after the products are released to the market. To this end, this work presents novel methods for ensuring the correctness of the microprocessor during the post-silicon validation phase and for improving the energy efficiency requirements of modern microprocessors. These methods can be applied during the prototyping phase of the microprocessors or after their release to the market. More specifically, in the first part of the thesis, we present and describe two different ISA-independent software-based post-silicon validation methods, which contribute to formalization and modeling as well as the acceleration of the post-silicon validation process and expose difficult-to-find bugs in the address translation mechanisms (ATM) of modern microprocessors. Both methods improve the detection and diagnosis of a hardware design bug in the ATM structures and significantly accelerate the bug detection during the post-silicon validation phase. In the second part of the thesis we present a detailed system-level voltage scaling characterization study for two state-of-the-art ARMv8-based multicore CPUs. We present an extensive characterization study which identifies the pessimistic voltage guardbands (the increased voltage margins set by the manufacturer) of each individual microprocessor core and analyze any abnormal behavior that may occur in off-nominal voltage conditions. Towards the formalization of the any abnormal behavior we also present a simple consolidated function; the Severity function, which aggregates the effects of reduced voltage operation. We then introduce the development of dedicated programs (diagnostic micro-viruses) that aim to accelerate the time-consuming voltage margins characterization studies by stressing the fundamental hardware components. Finally, we present a comprehensive exploration of how two server-grade systems behave in different frequency and core allocation configurations beyond nominal voltage operation in multicore executions. This analysis aims (1) to identify the best performance per watt operation points, (2) to reveal how and why the different core allocation options affect the energy consumption, and (3) to enhance the default Linux scheduler to take task allocation decisions for balanced performance and energy efficiency

KAVUAKA: a low-power application-specific processor architecture for digital hearing aids

The power consumption of digital hearing aids is very restricted due to their small physical size and the available hardware resources for signal processing are limited. However, there is a demand for more processing performance to make future hearing aids more useful and smarter. Future hearing aids should be able to detect, localize, and recognize target speakers in complex acoustic environments to further improve the speech intelligibility of the individual hearing aid user. Computationally intensive algorithms are required for this task. To maintain acceptable battery life, the hearing aid processing architecture must be highly optimized for extremely low-power consumption and high processing performance.The integration of application-specific instruction-set processors (ASIPs) into hearing aids enables a wide range of architectural customizations to meet the stringent power consumption and performance requirements. In this thesis, the application-specific hearing aid processor KAVUAKA is presented, which is customized and optimized with state-of-the-art hearing aid algorithms such as speaker localization, noise reduction, beamforming algorithms, and speech recognition. Specialized and application-specific instructions are designed and added to the baseline instruction set architecture (ISA). Among the major contributions are a multiply-accumulate (MAC) unit for real- and complex-valued numbers, architectures for power reduction during register accesses, co-processors and a low-latency audio interface. With the proposed MAC architecture, the KAVUAKA processor requires 16 % less cycles for the computation of a 128-point fast Fourier transform (FFT) compared to related programmable digital signal processors. The power consumption during register file accesses is decreased by 6 %to 17 % with isolation and by-pass techniques. The hardware-induced audio latency is 34 %lower compared to related audio interfaces for frame size of 64 samples.The final hearing aid system-on-chip (SoC) with four KAVUAKA processor cores and ten co-processors is integrated as an application-specific integrated circuit (ASIC) using a 40 nm low-power technology. The die size is 3.6 mm2. Each of the processors and co-processors contains individual customizations and hardware features with a varying datapath width between 24-bit to 64-bit. The core area of the 64-bit processor configuration is 0.134 mm2. The processors are organized in two clusters that share memory, an audio interface, co-processors and serial interfaces. The average power consumption at a clock speed of 10 MHz is 2.4 mW for SoC and 0.6 mW for the 64-bit processor.Case studies with four reference hearing aid algorithms are used to present and evaluate the proposed hardware architectures and optimizations. The program code for each processor and co-processor is generated and optimized with evolutionary algorithms for operation merging,instruction scheduling and register allocation. The KAVUAKA processor architecture is com-pared to related processor architectures in terms of processing performance, average power consumption, and silicon area requirements

Optimizing SIMD execution in HW/SW co-designed processors

SIMD accelerators are ubiquitous in microprocessors from different computing domains. Their high compute power and hardware simplicity improve overall performance in an energy efficient manner. Moreover, their replicated functional units and simple control mechanism make them amenable to scaling to higher vector lengths. However, code generation for these accelerators has been a challenge from the days of their inception. Compilers generate vector code conservatively to ensure correctness. As a result they lose significant vectorization opportunities and fail to extract maximum benefits out of SIMD accelerators. This thesis proposes to vectorize the program binary at runtime in a speculative manner, in addition to the compile time static vectorization. There are different environments that support runtime profiling and optimization support required for dynamic vectorization, one of most prominent ones being: 1) Dynamic Binary Translators and Optimizers (DBTO) and 2) Hardware/Software (HW/SW) Co-designed Processors. HW/SW co-designed environment provides several advantages over DBTOs like transparent incorporations of new hardware features, binary compatibility, etc. Therefore, we use HW/SW co-designed environment to assess the potential of speculative dynamic vectorization. Furthermore, we analyze vector code generation for wider vector units and find out that even though SIMD accelerators are amenable to scaling from the hardware point of view, vector code generation at higher vector length is even more challenging. The two major factors impeding vectorization for wider SIMD units are: 1) Reduced dynamic instruction stream coverage for vectorization and 2) Large number of permutation instructions. To solve the first problem we propose Variable Length Vectorization that iteratively vectorizes for multiple vector lengths to improve dynamic instruction stream coverage. Secondly, to reduce the number of permutation instructions we propose Selective Writing that selectively writes to different parts of a vector register and avoids permutations. Finally, we tackle the problem of leakage energy in SIMD accelerators. Since SIMD accelerators consume significant amount of real estate on the chip, they become the principle source of leakage if not utilized judiciously. Power gating is one of the most widely used techniques to reduce leakage energy of functional units. However, power gating has its own energy and performance overhead associated with it. We propose to selectively devectorize the vector code when higher SIMD lanes are used intermittently. This selective devectorization keeps the higher SIMD lanes idle and power gated for maximum duration. Therefore, resulting in overall leakage energy reduction.Postprint (published version

Predicting potential customer needs and wants for agile design and manufacture in an industry 4.0 environment

Manufacturing is currently experiencing a paradigm shift in the way that products are designed, produced and serviced. Such changes are brought about mainly by the extensive use of the Internet and digital technologies. As a result of this shift, a new industrial revolution is emerging, termed “Industry 4.0” (i4), which promises to accommodate mass customisation at a mass production cost. For i4 to become a reality, however, multiple challenges need to be addressed, highlighting the need for design for agile manufacturing and, for this, a framework capable of integrating big data analytics arising from the service end, business informatics through the manufacturing process, and artificial intelligence (AI) for the entire manufacturing value chain. This thesis attempts to address these issues, with a focus on the need for design for agile manufacturing. First, the state of the art in this field of research is reviewed on combining cutting-edge technologies in digital manufacturing with big data analysed to support agile manufacturing. Then, the work is focused on developing an AI-based framework to address one of the customisation issues in smart design and agile manufacturing, that is, prediction of potential customer needs and wants. With this framework, an AI-based approach is developed to predict design attributes that would help manufacturers to decide the best virtual designs to meet emerging customer needs and wants predictively. In particular, various machine learning approaches are developed to help explain at least 85% of the design variance when building a model to predict potential customer needs and wants. These approaches include k-means clustering, self-organizing maps, fuzzy k-means clustering, and decision trees, all supporting a vector machine to evaluate and extract conscious and subconscious customer needs and wants. A model capable of accurately predicting customer needs and wants for at least 85% of classified design attributes is thus obtained. Further, an analysis capable of determining the best design attributes and features for predicting customer needs and wants is also achieved. As the information analysed can be utilized to advise the selection of desired attributes, it is fed back in a closed-loop of the manufacturing value chain: design → manufacture → management/service → → → design... For this, a total of 4 case studies are undertaken to test and demonstrate the efficacy and effectiveness of the framework developed. These case studies include: 1) an evaluation model of consumer cars with multiple attributes including categorical and numerical ones; 2) specifications of automotive vehicles in terms of various characteristics including categorical and numerical instances; 3) fuel consumptions of various car models and makes, taking into account a desire for low fuel costs and low CO2 emissions; and 4) computer parts design for recommending the best design attributes when buying a computer. The results show that the decision trees, as a machine learning approach, work best in predicting customer needs and wants for smart design. With the tested framework and methodology, this thesis overall presents a holistic attempt to addressing the missing gap between manufacture and customisation, that is meeting customer needs and wants. Effective ways of achieving customization for i4 and smart manufacturing are identified. This is achieved through predicting potential customer needs and wants and applying the prediction at the product design stage for agile manufacturing to meet individual requirements at a mass production cost. Such agility is one key element in realising Industry 4.0. At the end, this thesis contributes to improving the process of analysing the data to predict potential customer needs and wants to be used as inputs to customizing product designs agilely

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

Adaptive Microarchitectural Optimizations to Improve Performance and Security of Multi-Core Architectures

With the current technological barriers, microarchitectural optimizations are increasingly important to ensure performance scalability of computing systems. The shift to multi-core architectures increases the demands on the memory system, and amplifies the role of microarchitectural optimizations in performance improvement. In a multi-core system, microarchitectural resources are usually shared, such as the cache, to maximize utilization but sharing can also lead to contention and lower performance. This can be mitigated through partitioning of shared caches.However, microarchitectural optimizations which were assumed to be fundamentally secure for a long time, can be used in side-channel attacks to exploit secrets, as cryptographic keys. Timing-based side-channels exploit predictable timing variations due to the interaction with microarchitectural optimizations during program execution. Going forward, there is a strong need to be able to leverage microarchitectural optimizations for performance without compromising security. This thesis contributes with three adaptive microarchitectural resource management optimizations to improve security and/or\ua0performance\ua0of multi-core architectures\ua0and a systematization-of-knowledge of timing-based side-channel attacks.\ua0We observe that to achieve high-performance cache partitioning in a multi-core system\ua0three requirements need to be met: i) fine-granularity of partitions, ii) locality-aware placement and iii) frequent changes. These requirements lead to\ua0high overheads for current centralized partitioning solutions, especially as the number of cores in the\ua0system increases. To address this problem, we present an adaptive and scalable cache partitioning solution (DELTA) using a distributed and asynchronous allocation algorithm. The\ua0allocations occur through core-to-core challenges, where applications with larger performance benefit will gain cache capacity. The\ua0solution is implementable in hardware, due to low computational complexity, and can scale to large core counts.According to our analysis, better performance can be achieved by coordination of multiple optimizations for different resources, e.g., off-chip bandwidth and cache, but is challenging due to the increased number of possible allocations which need to be evaluated.\ua0Based on these observations, we present a solution (CBP) for coordinated management of the optimizations: cache partitioning, bandwidth partitioning and prefetching.\ua0Efficient allocations, considering the inter-resource interactions and trade-offs, are achieved using local resource managers to limit the solution space.The continuously growing number of\ua0side-channel attacks leveraging\ua0microarchitectural optimizations prompts us to review attacks and defenses to understand the vulnerabilities of different microarchitectural optimizations. We identify the four root causes of timing-based side-channel attacks: determinism, sharing, access violation\ua0and information flow.\ua0Our key insight is that eliminating any of the exploited root causes, in any of the attack steps, is enough to provide protection.\ua0Based on our framework, we present a systematization of the attacks and defenses on a wide range of microarchitectural optimizations, which highlights their key similarities.\ua0Shared caches are an attractive attack surface for side-channel attacks, while defenses need to be efficient since the cache is crucial for performance.\ua0To address this issue, we present an adaptive and scalable cache partitioning solution (SCALE) for protection against cache side-channel attacks. The solution leverages randomness,\ua0and provides quantifiable and information theoretic security guarantees using differential privacy. The solution closes the performance gap to a state-of-the-art non-secure allocation policy for a mix of secure and non-secure applications