Chapter One – An Overview of Architecture-Level Power- and Energy-Efficient Design Techniques

Power dissipation and energy consumption became the primary design constraint for almost all computer systems in the last 15 years. Both computer architects and circuit designers intent to reduce power and energy (without a performance degradation) at all design levels, as it is currently the main obstacle to continue with further scaling according to Moore's law. The aim of this survey is to provide a comprehensive overview of power- and energy-efficient “state-of-the-art” techniques. We classify techniques by component where they apply to, which is the most natural way from a designer point of view. We further divide the techniques by the component of power/energy they optimize (static or dynamic), covering in that way complete low-power design flow at the architectural level. At the end, we conclude that only a holistic approach that assumes optimizations at all design levels can lead to significant savings.Peer ReviewedPostprint (published version

Efficient Scaling of Out-of-Order Processor Resources

Rather than improving single-threaded performance, with the dawn of the multi-core era, processor microarchitects have exploited Moore's law transistor scaling by increasing core density on a chip and increasing the number of thread contexts within a core. However, single-thread performance and efficiency is still very relevant in the power-constrained multi-core era, as increasing core counts do not yield corresponding performance improvements under real thermal and thread-level constraints. This dissertation provides a detailed study of register reference count structures and its application to both conventional and non-conventional, latency tolerant, out-of-order processors. Prior work has incorporated reference counting, but without a detailed implementation or energy model. This dissertation presents a working implementation of reference count structures and shows the overheads are low and can be recouped by the techniques enabled in high-performance out-of-order processors. A study of register allocation algorithms exploits register file occupancy to reduce power consumption by dynamically resizing the register file, which is especially important in the face of wider multi-threaded processors who require larger register files. Latency tolerance has been introduced as a technique to improve single threaded performance by removing cache-miss dependent instructions from the execution pipeline until the miss returns. This dissertation introduces a microarchitecture with a predictive approach to identify long-latency loads, and reduce the energy cost and overhead of scaling the instruction window inherent in latency tolerant microarchitectures. The key features include a front-end predictive slice-out mechanism and in-order queue structure along with mechanisms to reduce the energy cost and register-file usage of executing instructions. Cycle-level simulation shows improved performance and reduced energy delay for memory-bound workloads. Both techniques scale processor resources, addressing register file inefficiency and the allocation of processor resources to instructions during low ILP regions.Ph.D., Computer Engineering -- Drexel University, 201

Vector coprocessor sharing techniques for multicores: performance and energy gains

Vector Processors (VPs) created the breakthroughs needed for the emergence of computational science many years ago. All commercial computing architectures on the market today contain some form of vector or SIMD processing. Many high-performance and embedded applications, often dealing with streams of data, cannot efficiently utilize dedicated vector processors for various reasons: limited percentage of sustained vector code due to substantial flow control; inherent small parallelism or the frequent involvement of operating system tasks; varying vector length across applications or within a single application; data dependencies within short sequences of instructions, a problem further exacerbated without loop unrolling or other compiler optimization techniques. Additionally, existing rigid SIMD architectures cannot tolerate efficiently dynamic application environments with many cores that may require the runtime adjustment of assigned vector resources in order to operate at desired energy/performance levels. To simultaneously alleviate these drawbacks of rigid lane-based VP architectures, while also releasing on-chip real estate for other important design choices, the first part of this research proposes three architectural contexts for the implementation of a shared vector coprocessor in multicore processors. Sharing an expensive resource among multiple cores increases the efficiency of the functional units and the overall system throughput. The second part of the dissertation regards the evaluation and characterization of the three proposed shared vector architectures from the performance and power perspectives on an FPGA (Field-Programmable Gate Array) prototype. The third part of this work introduces performance and power estimation models based on observations deduced from the experimental results. The results show the opportunity to adaptively adjust the number of vector lanes assigned to individual cores or processing threads in order to minimize various energy-performance metrics on modern vector- capable multicore processors that run applications with dynamic workloads. Therefore, the fourth part of this research focuses on the development of a fine-to-coarse grain power management technique and a relevant adaptive hardware/software infrastructure which dynamically adjusts the assigned VP resources (number of vector lanes) in order to minimize the energy consumption for applications with dynamic workloads. In order to remove the inherent limitations imposed by FPGA technologies, the fifth part of this work consists of implementing an ASIC (Application Specific Integrated Circuit) version of the shared VP towards precise performance-energy studies involving high- performance vector processing in multicore environments

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.Siirretty Doriast

Energy analysis and optimisation techniques for automatically synthesised coprocessors

The primary outcome of this research project is the development of a methodology enabling fast automated early-stage power and energy analysis of configurable processors for system-on-chip platforms. Such capability is essential to the process of selecting energy efficient processors during design-space exploration, when potential savings are highest. This has been achieved by developing dynamic and static energy consumption models for the constituent blocks within the processors. Several optimisations have been identified, specifically targeting the most significant blocks in terms of energy consumption. Instruction encoding mechanism reduces both the energy and area requirements of the instruction cache; modifications to the multiplier unit reduce energy consumption during inactive cycles. Both techniques are demonstrated to offer substantial energy savings. The aforementioned techniques have undergone detailed evaluation and, based on the positive outcomes obtained, have been incorporated into Cascade, a system-on-chip coprocessor synthesis tool developed by Critical Blue, to provide automated analysis and optimisation of processor energy requirements. This thesis details the process of identifying and examining each method, along with the results obtained. Finally, a case study demonstrates the benefits of the developed functionality, from the perspective of someone using Cascade to automate the creation of an energy-efficient configurable processor for system-on-chip platforms

Energy-efficient hardware design based on high-level synthesis

This dissertation describes research activities broadly concerning the area of High-level synthesis (HLS), but more specifically, regarding the HLS-based design of energy-efficient hardware (HW) accelerators. HW accelerators, mostly implemented on FPGAs, are integral to the heterogeneous architectures employed in modern high performance computing (HPC) systems due to their ability to speed up the execution while dramatically reducing the energy consumption of computationally challenging portions of complex applications. Hence, the first activity was regarding an HLS-based approach to directly execute an OpenCL code on an FPGA instead of its traditional GPU-based counterpart. Modern FPGAs offer considerable computational capabilities while consuming significantly smaller power as compared to high-end GPUs. Several different implementations of the K-Nearest Neighbor algorithm were considered on both FPGA- and GPU-based platforms and their performance was compared. FPGAs were generally more energy-efficient than the GPUs in all the test cases. Eventually, we were also able to get a faster (in terms of execution time) FPGA implementation by using an FPGA-specific OpenCL coding style and utilizing suitable HLS directives. The second activity was targeted towards the development of a methodology complementing HLS to automatically derive power optimization directives (also known as "power intent") from a system-level design description and use it to drive the design steps after HLS, by producing a directive file written using the common power format (CPF) to achieve power shut-off (PSO) in case of an ASIC design. The proposed LP-HLS methodology reduces the design effort by enabling designers to infer low power information from the system-level description of a design rather than at the RTL. This methodology required a SystemC description of a generic power management module to describe the design context of a HW module also modeled in SystemC, along with the development of a tool to automatically produce the CPF file to accomplish PSO. Several test cases were considered to validate the proposed methodology and the results demonstrated its ability to correctly extract the low power information and apply it to achieve power optimization in the backend flow

DC-Patch: A Microarchitectural Fault Patching Technique for GPU Register Files

The ever-increasing parallelism demand of General-Purpose Graphics Processing Unit (GPGPU) applications pushes toward larger and more energy-hungry register files in successive GPU generations. Reducing the supply voltage beyond its safe limit is an effective way to improve the energy efficiency of register files. However, at these operating voltages, the reliability of the circuit is compromised. This work aims to tolerate permanent faults from process variations in large GPU register files operating below the safe supply voltage limit. To do so, this paper proposes a microarchitectural patching technique, DC-Patch, exploiting the inherent data redundancy of applications to compress registers at run-time with neither compiler assistance nor instruction set modifications. Instead of disabling an entire faulty register file entry, DC-Patch leverages the reliable cells within a faulty entry to store compressed register values. Experimental results show that, with more than a third of faulty register entries, DC-Patch ensures a reliable operation of the register file and reduces the energy consumption by 47% with respect to a conventional register file working at nominal supply voltage. The energy savings are 21% compared to a voltage noise smoothing scheme operating at the safe supply voltage limit. These benefits are obtained with less than 2 and 6% impact on the system performance and area, respectively