A comparison of heuristic algorithms for custom instruction selection

International audienc

Fuzzy logic based energy and throughput aware design space exploration for MPSoCs

Multicore architectures were introduced to mitigate the issue of increase in power dissipation with clock frequency. Introduction of deeper pipelines, speculative threading etc. for single core systems were not able to bring much increase in performance as compared to their associated power overhead. However for multicore architectures performance scaling with number of cores has always been a challenge. The Amdahl's law shows that the theoretical maximum speedup of a multicore architecture is not even close to the multiple of number of cores. With less amount of code in parallel having more number of cores for an application might just contribute in greater power dissipation instead of bringing some performance advantage. Therefore there is a need of an adaptive multicore architecture that can be tailored for the application in use for higher energy efficiency. In this paper a fuzzy logic based design space exploration technique is presented that is targeted to optimize a multicore architecture according to the workload requirements in order to achieve optimum balance between throughput and energy of the system

Automatic Design of Application Specific Instruction Set Extensions Through Dataflow Graph Exploration

General-purpose processors are often incapable of achieving the challenging cost, performance, and power demands of high-performance applications. To meet these demands, most systems employ a number of hardware accelerators to off-load the computationally demanding portions of the application. As an alternative to this strategy, we examine customizing the computation capabilities of a processor for a particular application. The processor is extended with hardware in the form of a set of custom function units and instruction set extensions. To effectively identify opportunities for creating custom hardware, a dataflow graph design space exploration engine heuristically identifies candidate computation subgraphs without artificially constraining their size or shape. The engine combines estimates of performance gain, cost, and inherent limitations of the processor to grow candidate graphs in profitable directions while pruning unprofitable paths. This paper describes the dataflow graph exploration engine and evaluates its effectiveness across a set of embedded applications.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44572/1/10766_2004_Article_476941.pd

PSOFuzz: Fuzzing Processors with Particle Swarm Optimization

Hardware security vulnerabilities in computing systems compromise the security defenses of not only the hardware but also the software running on it. Recent research has shown that hardware fuzzing is a promising technique to efficiently detect such vulnerabilities in large-scale designs such as modern processors. However, the current fuzzing techniques do not adjust their strategies dynamically toward faster and higher design space exploration, resulting in slow vulnerability detection, evident through their low design coverage. To address this problem, we propose PSOFuzz, which uses particle swarm optimization (PSO) to schedule the mutation operators and to generate initial input programs dynamically with the objective of detecting vulnerabilities quickly. Unlike traditional PSO, which finds a single optimal solution, we use a modified PSO that dynamically computes the optimal solution for selecting mutation operators required to explore new design regions in hardware. We also address the challenge of inefficient initial seed generation by employing PSO-based seed generation. Including these optimizations, our final formulation outperforms fuzzers without PSO. Experiments show that PSOFuzz achieves up to 15.25$\times$ speedup for vulnerability detection and up to 2.22$\times$ speedup for coverage compared to the state-of-the-art simulation-based hardware fuzzer.Comment: To be published in the proceedings of the ICCAD, 202

Automatic synthesis and optimization of chip multiprocessors

The microprocessor technology has experienced an enormous growth during the last decades. Rapid downscale of the CMOS technology has led to higher operating frequencies and performance densities, facing the fundamental issue of power dissipation. Chip Multiprocessors (CMPs) have become the latest paradigm to improve the power-performance efficiency of computing systems by exploiting the parallelism inherent in applications. Industrial and prototype implementations have already demonstrated the benefits achieved by CMPs with hundreds of cores.CMP architects are challenged to take many complex design decisions. Only a few of them are:- What should be the ratio between the core and cache areas on a chip?- Which core architectures to select?- How many cache levels should the memory subsystem have?- Which interconnect topologies provide efficient on-chip communication?These and many other aspects create a complex multidimensional space for architectural exploration. Design Automation tools become essential to make the architectural exploration feasible under the hard time-to-market constraints. The exploration methods have to be efficient and scalable to handle future generation on-chip architectures with hundreds or thousands of cores.Furthermore, once a CMP has been fabricated, the need for efficient deployment of the many-core processor arises. Intelligent techniques for task mapping and scheduling onto CMPs are necessary to guarantee the full usage of the benefits brought by the many-core technology. These techniques have to consider the peculiarities of the modern architectures, such as availability of enhanced power saving techniques and presence of complex memory hierarchies.This thesis has several objectives. The first objective is to elaborate the methods for efficient analytical modeling and architectural design space exploration of CMPs. The efficiency is achieved by using analytical models instead of simulation, and replacing the exhaustive exploration with an intelligent search strategy. Additionally, these methods incorporate high-level models for physical planning. The related contributions are described in Chapters 3, 4 and 5 of the document.The second objective of this work is to propose a scalable task mapping algorithm onto general-purpose CMPs with power management techniques, for efficient deployment of many-core systems. This contribution is explained in Chapter 6 of this document.Finally, the third objective of this thesis is to address the issues of the on-chip interconnect design and exploration, by developing a model for simultaneous topology customization and deadlock-free routing in Networks-on-Chip. The developed methodology can be applied to various classes of the on-chip systems, ranging from general-purpose chip multiprocessors to application-specific solutions. Chapter 7 describes the proposed model.The presented methods have been thoroughly tested experimentally and the results are described in this dissertation. At the end of the document several possible directions for the future research are proposed

Evaluation of Different Manual Placement Strategies to Ensure Uniformity of the V-FPGA

Virtual FPGA (V-FPGA) architectures are useful as both early prototyping testbeds for custom FPGA architectures, as well as to enable advanced features which may not be available on a given host FPGA. V-FPGAs use standard FPGA synthesis and placement tools, and as a result the maximum application frequency is largely determined by the synthesis of the V-FPGA onto the host FPGA. Minimal net delays in the virtual layer are crucial for applications, but due to increased routing congestion, these delays are often significantly worse for larger than for smaller designs. To counter this effect, we investigate three different placement strategies with varying amounts of manual intervention. Taking the regularity of the V-FPGA architecture into account, a regular placement of tiles can lead to an 37% improvement in the achievable clock frequency. In addition, uniformity of the measured net delays is increased by 39%, which makes implementation of user applications more reproducible. As a trade-off, these manual placement strategies increase area usage of the virtual layer up to 16%

Optimierung der Energie und Power getriebenen Architekturexploration für Multicore und heterogenes System on Chip

The contribution of this work builds on top of the established virtual prototype platforms to improve both SoC design quality and productivity. Initially, an automatic system-level power estimation framework was developed to address the critical issue of early power estimation in SoC design. The estimation framework models the static and dynamic power consumption of the hardware components. These models are created from the normalized values of the basic design components of SoC, obtained through one-time power simulation of RTL hardware models. The framework allows dynamic technology node reconfiguration for power estimation models. Its instantaneous power reporting aids the detection of possible hotspot early into the design process. Adding this additional data in conjunction with a steadily growing design space of complex heterogeneous SoC, finding the right parameter configuration is a challenging and laborious task for a system-level designer. This work addresses this bottleneck by optimizing the design space exploration (DSE) process for MPSoC design. An automatic DSE framework for virtual platforms (VPs) was developed which is flexible and allows the selection optimal parameter configuration without pre-existing knowledge. To reduce exploration time, the framework is equipped with several multi-objective optimization techniques based on simulated annealing and a genetic algorithm. Lastly, to aid HW/SW partitioning at system-level, a flexible and automated workflow (SW2TLM) is presented. It allows the designer to explore various possible partitioning scenarios without going into depth of the hardware architecture complexity and software integration. The framework generates system-level hardware accelerators from corresponding functionality encoded in the software code and integrates them into the VP. Power consumption and time speedups of acceleration is reported to the designer, which further increases the quality and productivity of the development process towards the final architecture. The presented tools are evaluated using a state-of-the-art VP for a range of single and multi-core applications. Viewing the energy delay product, a reduction in exploration time was recorded at approximately 62% (worst case), maintaining optimal parameter accuracy of 90% compared to previous techniques. While the SW2TLM further increases the exploration versatility by combining modern high-level synthesis with system-level architectural exploration.Der Beitrag dieser Arbeit baut auf dem etablierten Konzept der virtuellen Prototyp (VP) Plattformen auf, um die Qualität und die Produktivität des Entwurfsprozesses zu verbessern. Zunächst wurde ein automatisches System-Level-Framework entwickelt, um Verlustleistungsabschätzung für SoC-Designs in einer deutlich früheren Entwicklungsphase zu ermöglichen. Hierfür werden statischen und dynamischen Energieverbrauchsanteile individueller Hardwareelemente durch ein abstraktes Modell ausgedrückt. Das Framework ermöglicht eine dynamische Anpassung des Technologieknotens sowie die Integration neuer Leistungsmodelle für Drittanbieterkomponenten. Die kontinuierliche Erfassung der Energieverbrauchseigenschaften und ihre grafische Darstellung Benutzeroberfläche unterstützt zusätzlich die frühzeitige Identifikation möglicher Hotspots. Durch die Bereitstellung zusätzlicher Daten, in Verbindung mit einem stetig wachsenden Entwurfsraum komplexer SoCs, ist die Identifikation der richtigen Parameterkonfiguration eine zeitintensive Aufgabe. Die vorgelegten Konzepte erlauben eine gesteigerte Automatisierung des Explorationsprozesses. Techniken der mehrdimensionalen Optimierung, basierend auf Simulated Annealing und genetischer Algorithmen erlauben die Identifikation von geeigneten Konfigurationen ohne vorheriges Wissen oder Erfahrungswerte Schließlich wurde zur Unterstützung der HW/SW -Partitionierung auf System-Ebene ein flexibler und automatisierter Workflow entwickelt. Er ermöglicht es dem Designer verschiedene mögliche Partitionierungsszenarien zu untersuchen, ohne sich in die Komplexität der Hardwarearchitektur und der Softwareintegration zu vertiefen. Das Framework erzeugt abstrakte Beschleunigermodelle aus entsprechenden Softwarefunktionen und integriert sie nahtlos in den ausführbare VP. Detaillierte Daten zum Energieverbrauch, Beschleunigungsfaktor und Kommunikationsoverhead der Partitionierung werden erfasst und dem Designer zur Verfügung gestellt, was die Qualität und Produktivität des weiter erhöht. Die vorgestellten Tools werden mit einer modernen VP für verschiedene SW-Anwendungen evaluiert. Bei Betrachtung des Energieverzögerungsprodukts wurde eine Verringerung der Explorationszeit um mehr als 62% bei 90% Parametergenauigkeit festgestell. Darauf aufbauend, erleichtert die automatisierte Untersuchung verschiedener HW/SW Partitionierungen die Entwicklung heterogener Architekturen durch die Kombination moderner HLS mit Architektur-Exploration auf der Systemebene

STATISTICAL MACHINE LEARNING BASED MODELING FRAMEWORK FOR DESIGN SPACE EXPLORATION AND RUN-TIME CROSS-STACK ENERGY OPTIMIZATION FOR MANY-CORE PROCESSORS

The complexity of many-core processors continues to grow as a larger number of heterogeneous cores are integrated on a single chip. Such systems-on-chip contains computing structures ranging from complex out-of-order cores, simple in-order cores, digital signal processors (DSPs), graphic processing units (GPUs), application specific processors, hardware accelerators, I/O subsystems, network-on-chip interconnects, and large caches arranged in complex hierarchies. While the industry focus is on putting higher number of cores on a single chip, the key challenge is to optimally architect these many-core processors such that performance, energy and area constraints are satisfied. The traditional approach to processor design through extensive cycle accurate simulations are ill-suited for designing many-core processors due to the large microarchitecture design space that must be explored. Additionally it is hard to optimize such complex processors and the applications that run on them statically at design time such that performance and energy constraints are met under dynamically changing operating conditions. The dissertation establishes statistical machine learning based modeling framework that enables the efficient design and operation of many-core processors that meets performance, energy and area constraints. We apply the proposed framework to rapidly design the microarchitecture of a many-core processor for multimedia, computer graphics rendering, finance, and data mining applications derived from the Parsec benchmark. We further demonstrate the application of the framework in the joint run-time adaptation of both the application and microarchitecture such that energy availability constraints are met

Runtime Management of Multiprocessor Systems for Fault Tolerance, Energy Efficiency and Load Balancing

Efficiency of modern multiprocessor systems is hurt by unpredictable events: aging causes permanent faults that disable components; application spawnings and terminations taking place at arbitrary times, affect energy proportionality, causing energy waste; load imbalances reduce resource utilization, penalizing performance. This thesis demonstrates how runtime management can mitigate the negative effects of unpredictable events, making decisions guided by a combination of static information known in advance and parameters that only become known at runtime. We propose techniques for three different objectives: graceful degradation of aging-prone systems; energy efficiency of heterogeneous adaptive systems; and load balancing by means of work stealing. Managing aging-prone systems for graceful efficiency degradation, is based on a high-level system description that encapsulates hardware reconfigurability and workload flexibility and allows to quantify system efficiency and use it as an objective function. Different custom heuristics, as well as simulated annealing and a genetic algorithm are proposed to optimize this objective function as a response to component failures. Custom heuristics are one to two orders of magnitude faster, provide better efficiency for the first 20% of system lifetime and are less than 13% worse than a genetic algorithm at the end of this lifetime. Custom heuristics occasionally fail to satisfy reconfiguration cost constraints. As all algorithms\u27 execution time scales well with respect to system size, a genetic algorithm can be used as backup in these cases. Managing heterogeneous multiprocessors capable of Dynamic Voltage and Frequency Scaling is based on a model that accurately predicts performance and power: performance is predicted by combining static, application-specific profiling information and dynamic, runtime performance monitoring data; power is predicted using the aforementioned performance estimations and a set of platform-specific, static parameters, determined only once and used for every application mix. Three runtime heuristics are proposed, that make use of this model to perform partial search of the configuration space, evaluating a small set of configurations and selecting the best one. When best-effort performance is adequate, the proposed approach achieves 3% higher energy efficiency compared to the powersave governor and 2x better compared to the interactive and ondemand governors. When individual applications\u27 performance requirements are considered, the proposed approach is able to satisfy them, giving away 18% of system\u27s energy efficiency compared to the powersave, which however misses the performance targets by 23%; at the same time, the proposed approach maintains an efficiency advantage of about 55% compared to the other governors, which also satisfy the requirements. Lastly, to improve load balancing of multiprocessors, a partial and approximate view of the current load distribution among system cores is proposed, which consists of lightweight data structures and is maintained by each core through cheap operations. A runtime algorithm is developed, using this view whenever a core becomes idle, to perform victim core selection for work stealing, also considering system topology and memory hierarchy. Among 12 diverse imbalanced workloads, the proposed approach achieves better performance than random, hierarchical and local stealing for six workloads. Furthermore, it is at most 8% slower among the other six workloads, while competing strategies incur a penalty of at least 89% on some workload