Energy-Aware Data Movement In Non-Volatile Memory Hierarchies

While technology scaling enables increased density for memory cells, the intrinsic high leakage power of conventional CMOS technology and the demand for reduced energy consumption inspires the use of emerging technology alternatives such as eDRAM and Non-Volatile Memory (NVM) including STT-MRAM, PCM, and RRAM. The utilization of emerging technology in Last Level Cache (LLC) designs which occupies a signifcant fraction of total die area in Chip Multi Processors (CMPs) introduces new dimensions of vulnerability, energy consumption, and performance delivery. To be specific, a part of this research focuses on eDRAM Bit Upset Vulnerability Factor (BUVF) to assess vulnerable portion of the eDRAM refresh cycle where the critical charge varies depending on the write voltage, storage and bit-line capacitance. This dissertation broaden the study on vulnerability assessment of LLC through investigating the impact of Process Variations (PV) on narrow resistive sensing margins in high-density NVM arrays, including on-chip cache and primary memory. Large-latency and power-hungry Sense Amplifers (SAs) have been adapted to combat PV in the past. Herein, a novel approach is proposed to leverage the PV in NVM arrays using Self-Organized Sub-bank (SOS) design. SOS engages the preferred SA alternative based on the intrinsic as-built behavior of the resistive sensing timing margin to reduce the latency and power consumption while maintaining acceptable access time. On the other hand, this dissertation investigates a novel technique to prioritize the service to 1) Extensive Read Reused Accessed blocks of the LLC that are silently dropped from higher levels of cache, and 2) the portion of the working set that may exhibit distant re-reference interval in L2. In particular, we develop a lightweight Multi-level Access History Profiler to effciently identify ERRA blocks through aggregating the LLC block addresses tagged with identical Most Signifcant Bits into a single entry. Experimental results indicate that the proposed technique can reduce the L2 read miss ratio by 51.7% on average across PARSEC and SPEC2006 workloads. In addition, this dissertation will broaden and apply advancements in theories of subspace recovery to pioneer computationally-aware in-situ operand reconstruction via the novel Logic In Interconnect (LI2) scheme. LI2 will be developed, validated, and re?ned both theoretically and experimentally to realize a radically different approach to post-Moore\u27s Law computing by leveraging low-rank matrices features offering data reconstruction instead of fetching data from main memory to reduce energy/latency cost per data movement. We propose LI2 enhancement to attain high performance delivery in the post-Moore\u27s Law era through equipping the contemporary micro-architecture design with a customized memory controller which orchestrates the memory request for fetching low-rank matrices to customized Fine Grain Reconfigurable Accelerator (FGRA) for reconstruction while the other memory requests are serviced as before. The goal of LI2 is to conquer the high latency/energy required to traverse main memory arrays in the case of LLC miss, by using in-situ construction of the requested data dealing with low-rank matrices. Thus, LI2 exchanges a high volume of data transfers with a novel lightweight reconstruction method under specific conditions using a cross-layer hardware/algorithm approach

MeRLiN: Exploiting dynamic instruction behavior for fast and accurate microarchitecture level reliability assessment

Early reliability assessment of hardware structures using microarchitecture level simulators can effectively guide major error protection decisions in microprocessor design. Statistical fault injection on microarchitectural structures modeled in performance simulators is an accurate method to measure their Architectural Vulnerability Factor (AVF) but requires excessively long campaigns to obtain high statistical significance. We propose MeRLiN1, a methodology to boost microarchitecture level injection-based reliability assessment by several orders of magnitude and keep the accuracy of the assessment unaffected even for large injection campaigns with very high statistical significance. The core of MeRLiN is the grouping of faults of an initial list in equivalent classes. All faults in the same group target equivalent vulnerable intervals of program execution ending up to the same static instruction that reads the faulty entries. Faults in the same group occur in different times and entries of a structure and it is extremely likely that they all have the same effect in program execution; thus, fault injection is performed only on a few representatives from each group. We evaluate MeRLiN for different sizes of the physical register file, the store queue and the first level data cache of a contemporary microarchitecture running MiBench and SPEC CPU2006 benchmarks. For all our experiments, MeRLiN is from 2 to 3 orders of magnitude faster than an extremely high statistical significant injection campaign, reporting the same reliability measurements with negligible loss of accuracy. Finally, we theoretically analyze MeRLiN's statistical behavior to further justify its accuracy.Peer ReviewedPostprint (author's final draft

Methodologies for Accelerated Analysis of the Reliability and the Energy Efficiency Levels of Modern Microprocessor Architectures

Η εξέλιξη της τεχνολογίας ημιαγωγών, της αρχιτεκτονικής υπολογιστών και της σχεδίασης οδηγεί σε αύξηση της απόδοσης των σύγχρονων μικροεπεξεργαστών, η οποία επίσης συνοδεύεται από αύξηση της ευπάθειας των προϊόντων. Οι σχεδιαστές εφαρμόζουν διάφορες τεχνικές κατά τη διάρκεια της ζωής των ολοκληρωμένων κυκλωμάτων με σκοπό να διασφαλίσουν τα υψηλά επίπεδα αξιοπιστίας των παραγόμενων προϊόντων και να τα προστατέψουν από διάφορες κατηγορίες σφαλμάτων διασφαλίζοντας την ορθή λειτουργία τους. Αυτή η διδακτορική διατριβή προτείνει καινούριες μεθόδους για να διασφαλίσει τα υψηλά επίπεδα αξιοπιστίας και ενεργειακής απόδοσης των σύγχρονων μικροεπεξεργαστών οι οποίες μπορούν να εφαρμοστούν κατά τη διάρκεια του πρώιμου σχεδιαστικού σταδίου, του σταδίου παραγωγής ή του σταδίου της κυκλοφορίας των ολοκληρωμένων κυκλωμάτων στην αγορά. Οι συνεισφορές αυτής της διατριβής μπορούν να ομαδοποιηθούν στις ακόλουθες δύο κατηγορίες σύμφωνα με το στάδιο της ζωής των μικροεπεξεργαστών στο οποίο εφαρμόζονται: • Πρώιμο σχεδιαστικό στάδιο: Η στατιστική εισαγωγή σφαλμάτων σε δομές που είναι μοντελοποιημένες σε προσομοιωτές οι οποίοι στοχεύουν στην μελέτη της απόδοσης είναι μια επιστημονικά καθιερωμένη μέθοδος για την ακριβή μέτρηση της αξιοπιστίας, αλλά υστερεί στον αργό χρόνο εκτέλεσης. Σε αυτή τη διατριβή, αρχικά παρουσιάζουμε ένα νέο πλήρως αυτοματοποιημένο εργαλείο εισαγωγής σφαλμάτων σε μικροαρχιτεκτονικό επίπεδο που στοχεύει στην ακριβή αξιολόγηση της αξιοπιστίας ενός μεγάλου πλήθους μονάδων υλικού σε σχέση με διάφορα μοντέλα σφαλμάτων (παροδικά, διακοπτόμενα, μόνιμα σφάλματα). Στη συνέχεια, χρησιμοποιώντας το ίδιο εργαλείο και στοχεύοντας τα παροδικά σφάλματα, παρουσιάζουμε διάφορες μελέτες σχετιζόμενες με την αξιοπιστία και την απόδοση, οι οποίες μπορούν να βοηθήσουν τις σχεδιαστικές αποφάσεις στα πρώιμα στάδια της ζωής των επεξεργαστών. Τελικά, προτείνουμε δύο μεθοδολογίες για να επιταχύνουμε τα μαζικά πειράματα στατιστικής εισαγωγής σφαλμάτων. Στην πρώτη, επιταχύνουμε τα πειράματα έπειτα από την πραγματική εισαγωγή των σφαλμάτων στις δομές του υλικού. Στη δεύτερη, επιταχύνουμε ακόμη περισσότερο τα πειράματα προτείνοντας τη μεθοδολογία με όνομα MeRLiN, η οποία βασίζεται στη μείωση της αρχικής λίστας σφαλμάτων μέσω της ομαδοποίησής τους σε ισοδύναμες ομάδες έπειτα από κατηγοριοποίηση σύμφωνα με την εντολή που τελικά προσπελαύνει τη δομή που φέρει το σφάλμα. • Παραγωγικό στάδιο και στάδιο κυκλοφορίας στην αγορά: Οι συνεισφορές αυτής της διδακτορικής διατριβής σε αυτά τα στάδια της ζωής των μικροεπεξεργαστών καλύπτουν δύο σημαντικά επιστημονικά πεδία. Αρχικά, χρησιμοποιώντας το ολοκληρωμένο κύκλωμα των 48 πυρήνων με ονομασία Intel SCC, προτείνουμε μια τεχνική επιτάχυνσης του εντοπισμού μονίμων σφαλμάτων που εφαρμόζεται κατά τη διάρκεια λειτουργίας αρχιτεκτονικών με πολλούς πυρήνες, η οποία εκμεταλλεύεται το δίκτυο υψηλής ταχύτητας μεταφοράς μηνυμάτων που διατίθεται στα ολοκληρωμένα κυκλώματα αυτού του είδους. Δεύτερον, προτείνουμε μια λεπτομερή στατιστική μεθοδολογία με σκοπό την ακριβή πρόβλεψη σε επίπεδο συστήματος των ασφαλών ορίων λειτουργίας της τάσης των πυρήνων τύπου ARMv8 που βρίσκονται πάνω στη CPU X-Gene 2.The evolution in semiconductor manufacturing technology, computer architecture and design leads to increase in performance of modern microprocessors, which is also accompanied by increase in products’ vulnerability to errors. Designers apply different techniques throughout microprocessors life-time in order to ensure the high reliability requirements of the delivered products that are defined as their ability to avoid service failures that are more frequent and more severe than is acceptable. This thesis proposes novel methods to guarantee the high reliability and energy efficiency requirements of modern microprocessors that can be applied during the early design phase, the manufacturing phase or after the chips release to the market. The contributions of this thesis can be grouped in the two following categories according to the phase of the CPUs lifecycle that are applied at: • Early design phase: Statistical fault injection using microarchitectural structures modeled in performance simulators is a state-of-the-art method to accurately measure the reliability, but suffers from low simulation throughput. In this thesis, we firstly present a novel fully-automated versatile microarchitecture-level fault injection framework (called MaFIN) for accurate characterization of a wide range of hardware components of an x86-64 microarchitecture with respect to various fault models (transient, intermittent, permanent faults). Next, using the same tool and focusing on transient faults, we present several reliability and performance related studies that can assist design decision in the early design phases. Moreover, we propose two methodologies to accelerate the statistical fault injection campaigns. In the first one, we accelerate the fault injection campaigns after the actual injection of the faults in the simulated hardware structures. In the second, we further accelerate the microarchitecture level fault injection campaigns by proposing MeRLiN a fault pre-processing methodology that is based on the pruning of the initial fault list by grouping the faults in equivalent classes according to the instruction access patterns to hardware entries. • Manufacturing phase and release to the market: The contributions of this thesis in these phases of microprocessors life-cycle cover two important aspects. Firstly, using the 48-core Intel’s SCC architecture, we propose a technique to accelerate online error detection of permanent faults for many-core architectures by exploiting their high-speed message passing on-chip network. Secondly, we propose a comprehensive statistical analysis methodology to accurately predict at the system level the safe voltage operation margins of the ARMv8 cores of the X- Gene 2 chip when it operates in scaled voltage conditions

NFV Platforms: Taxonomy, Design Choices and Future Challenges

Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm. During the last few years, a large number of NFV platforms have been implemented in production environments that typically face critical challenges, including the development, deployment, and management of Virtual Network Functions (VNFs). Nonetheless, just like any complex system, such platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore their design space. In this paper, we present a comprehensive survey on the NFV platform design. Our study solely targets existing NFV platform implementations. We begin with a top-down architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on what features they provide in terms of a typical network function life cycle. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We also envision future challenges for NFV platform design in the incoming 5G era. We believe that our study gives a detailed guideline for network operators or service providers to choose the most appropriate NFV platform based on their respective requirements. Our work also provides guidelines for implementing new NFV platforms

Improving Model-Based Software Synthesis: A Focus on Mathematical Structures

Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis. We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general. This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking. We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability. The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures

Workflow models for heterogeneous distributed systems

The role of data in modern scientific workflows becomes more and more crucial. The unprecedented amount of data available in the digital era, combined with the recent advancements in Machine Learning and High-Performance Computing (HPC), let computers surpass human performances in a wide range of fields, such as Computer Vision, Natural Language Processing and Bioinformatics. However, a solid data management strategy becomes crucial for key aspects like performance optimisation, privacy preservation and security. Most modern programming paradigms for Big Data analysis adhere to the principle of data locality: moving computation closer to the data to remove transfer-related overheads and risks. Still, there are scenarios in which it is worth, or even unavoidable, to transfer data between different steps of a complex workflow. The contribution of this dissertation is twofold. First, it defines a novel methodology for distributed modular applications, allowing topology-aware scheduling and data management while separating business logic, data dependencies, parallel patterns and execution environments. In addition, it introduces computational notebooks as a high-level and user-friendly interface to this new kind of workflow, aiming to flatten the learning curve and improve the adoption of such methodology. Each of these contributions is accompanied by a full-fledged, Open Source implementation, which has been used for evaluation purposes and allows the interested reader to experience the related methodology first-hand. The validity of the proposed approaches has been demonstrated on a total of five real scientific applications in the domains of Deep Learning, Bioinformatics and Molecular Dynamics Simulation, executing them on large-scale mixed cloud-High-Performance Computing (HPC) infrastructures

Compiler-driven data layout transformations for network applications

This work approaches the little studied topic of compiler optimisations directed to network applications. It starts by investigating if there exist any fundamental differences between application domains that justify the development and tuning of domain-specific compiler optimisations. It shows an automated approach that is capable of identifying domain-specific workload characterisations and presenting them in a readily interpretable format based on decision trees. The generated workload profiles summarise key resource utilisation issues and enable compiler engineers to address the highlighted bottlenecks. By applying this methodology to data intensive network infrastructure application it shows that data organisation is the key obstacle to overcome in order to achieve high performance. It therefore proposes and evaluates three specialised data transformations (structure splitting, array regrouping, and software caching) against the industrial EEMBC networking benchmarks and real-world data sets. It also demonstrates on one hand that speedups of up to 2.62 can be achieved, but on the other that no single solution performs equally well across different network traffic scenarios. Hence, to address this issue, an adaptive software caching scheme for high frequency route lookup operations is introduced and its effectiveness evaluated one more time against EEMBC networking benchmarks and real-world data sets achieving speedups of up to 3.30 and 2.27. The results clearly demonstrate that adaptive data organisation schemes are necessary to ensure optimal performance under varying network loads. Finally this research addresses another issue introduced by data transformations such as array regrouping and software caching, i.e. the need for static analysis to allow efficient resource allocation. This thesis proposes a static code analyser that allows the automatic resource analysis of source code containing lists and tree structures. The tool applies a combination of amortised analysis and separation logic methodology to real code and is able to evaluate type and resource usage of existing data structures, which can be used to compute global resource consumption values for full data intensive network applications

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

Tools for efficient Deep Learning

In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption. We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work. This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C. Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets. All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces