Coarse-grained reconfigurable array architectures

Coarse-Grained Reconfigurable Array (CGRA) architectures accelerate the same inner loops that benefit from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efficiently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on flexibility, performance, and power-efficiency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual fine-tuning of source code

재구성형 연산 구조를 위한 부동소수점 지원

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 최기영.With a huge increase in demand for various kinds of compute-intensive applications in electronic systems, researchers have focused on coarse-grained reconfigurable architectures because of their advantages: high performance and flexibility. Besides, supporting floating-point operations on coarse-grained reconfigurable architecture becomes essential as the increase of demands on various floating-point inclusive applications such as multimedia processing, 3D graphics, augmented reality, or object recognition. This thesis presents FloRA, a coarse-grained reconfigurable architecture with floating-point support. Two-dimensional array of integer processing elements in FloRA is configured at run-time to perform floating-point operations as well as integer operations. More specifically, each floating-point operation is performed by two integer processing elements, one for mantissa and the other for exponent. Fabricated using 130nm process, the total area overhead due to additional hardware for floating-point operations is about 7.4% compared to the previous architecture which does not support floating-point operations. The fabricated chip runs at 125MHz clock frequency and 1.2V power supply. Experiments show 11.6x speedup on average compared to ARM9 with a vector-floating-point unit for integer-only benchmark programs as well as programs containing floating-point operations. Compared with other similar approaches including XPP and Butter, the proposed architecture shows much higher performance for integer applications, while maintaining about half the performance of Butter for floating-point applications. This thesis also proposes novel techniques to enhance utilization of integer units for high-throughput floating-point operations on CGRA. The approach to implementing floating-point operations on CGRA presented in this thesis enables floating-point functionality with less area overhead compared to the traditional approach of employing separate floating-point units (FPUs). However the total latency of a floating-point operation is larger than that of the traditional approach and the data dependency between split integer operations restricts further enhancement in terms of utilization of integer functional units in an operation. In order to overcome such inefficiency, two techniques are proposed in this thesis. One is overlapping two distinct floating-point operations, which increases the efficiency in terms of utilizations of integer functional units in the architecture. Free integer functional units in a floating-point operation can be used for another floating-point operation with this technique. The other is forwarding between two data-dependent floating-point operations, which decreases effective latency of the floating-point operations. The basic idea is to remove unnecessary calculations such as formatting which is normally done in between the two data-dependent floating-point operations. To implement the overlapping or forwarding, FSMs and control paths in each PE are modified and temporal/communication registers are added. Light-weight sub-module such as increment units and registers for intermediate values are added for releasing resource conflict. Experiment is done with several arithmetic functions that are widely used in floating-point applications. The base architecture and the new architecture implementing the proposed technique are compared in terms of throughput and area overhead. The experimental result shows that the proposed technique increases the throughput by 33.9% on average with 20.9% of area overhead.Abstract i Contents v List of Figures ix List of Tables xv Chapter 1 INTRODUCTION 1 Chapter 2 TARGET ARCHITECTURE 7 2.1 Overall Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Reconfigurable Computing Module . . . . . . . . . . . . . . . . . 8 Chapter 3 DEGISN OF FLOATING-POINT OPERATIONS 15 3.1 Floating-point Numbers . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.1 Representation of floating-point numbers . . . . . . . . . . 15 3.1.2 Floating-point operations . . . . . . . . . . . . . . . . . . . 19 3.2 FPU-PE Cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.1 Construction of FPU-PE Cluster . . . . . . . . . . . . . . . 20 3.2.2 Construction of Array of FPU-PE Clusters . . . . . . . . . 21 3.2.3 Comparing Different FPU-PE Clusters . . . . . . . . . . . 23 3.3 Implementation of Multi-Cycle Operations . . . . . . . . . . . . 26 3.4 Implementation of Floating-Point Operations . . . . . . . . . . . 30 3.5 Implementation of Floating-Point Operations Using Shared Modules . . . 32 Chapter 4 Chip Implementation 35 4.1 Specification of Chip Implementation . . . . . . . . . . . . . . . . 35 4.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.3 Experimantal Results . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3.1 Performance Comparison . . . . . . . . . . . . . . . . . . . 39 4.3.2 Power Consumption Comparison . . . . . . . . . . . . . . 42 Chapter 5 Comparison with Other Architectures 45 5.1 Preparation for the comparison . . . . . . . . . . . . . . . . . . . 45 5.2 Comparison with PACT XPP . . . . . . . . . . . . . . . . . . . . . 47 5.3 Comparison with Butter Architecture . . . . . . . . . . . . . . . . 50 5.4 Implication of the proposed architecture . . . . . . . . . . . . . . 57 Chapter 6 Enhancement Techniques 63 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.2 Conventional Approach . . . . . . . . . . . . . . . . . . . . . . . 64 6.2.1 Base Architecture . . . . . . . . . . . . . . . . . . . . . . . 64 6.2.2 Utilization of Floating-Point Operations . . . . . . . . . . 65 6.3 Proposed Enhancement Techniques . . . . . . . . . . . . . . . . . 66 6.3.1 Overlapping Technique . . . . . . . . . . . . . . . . . . . . 66 6.3.2 Forwarding Technique . . . . . . . . . . . . . . . . . . . . . 71 6.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.4.1 Performance Comparison . . . . . . . . . . . . . . . . . . . 76 6.4.2 Hardware Cost of the Proposed Techniques . . . . . . . . . 77 6.4.3 Utilization Enhancement by the Proposed Techniques . . . 80 6.5 Comparison with Other Architecture . . . . . . . . . . . . . . . . 87 Chapter 7 Conclusion 93 Bibliography 95 국문초록 103 감사의 글 105Docto

재구성형 구조에서의 효율적인 조건실행 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 최기영.재구성형 구조는 연산량이 많은 프로그램을 내장형 시스템에서 가속시키는 데 적합한 방법 중 하나이다. 이는 일반적으로 많은 연산유닛들과 하나의 컨트롤러로 구성되어 고성능, 유연성, 저전력을 동시에 달성할 수 있도록 해준다. 많은 연산유닛을 바탕으로 한 병렬처리는 응용프로그램의 실행속도를 빠르게 하며, 재구성 기능은 다양한 응용프로그램에의 활용을 가능하게 해준다. 또한, 명령어와 데이터에 대한 스케쥴을 미리 정해놓음으로써 제어구조를 단순화시킬 수 있으며 이는 연산량 대비 전력소모를 최소한으 로 줄여준다. 하지만 응용프로그램이 복잡해짐에 따라 연산량이 많은 부분들에 분기문이 생기게 되었으며 이는 재구성형 구조를 사용함에 있어 큰 위협이 되고 있다. 분기문을 다룰 수 있는 컨트롤러가 하나이기 때문에 컨트롤러에 병목현상이 발생하거나 동시에 서로 다른 제어를 요구하게 되면 해당 프로그램은 가속이 불가능해진다. 조건실행이라는 기술을 사용할 경우 이를 부분적으로 해소할 수 있지만 기존에 개발되어 있는 조건실행 기술들은 재구성형 구조에 성능 및 전력소모 면에서 부정적인 영향을 끼친다. 따라서 본 논문에서는 연산량이 많지만 분기문을 가진 응용프로그램에서 조건실행이 성능과 전력 면에서 어떠한 영향을 미치는지 밝히며 이를 바탕으로 고성능과 저전력을 가진 조건실행 방법을 제안한다. 실험 결과에 따르면 제안한 방식은 기존의 세가지 방식보다 성능과 전력소모를 곱으로 표현한 수치에 있어서 11.9%, 14.7%, 23.8% 만큼의 이득을 보였다. 또한, 제안한 조건실행 방법에 적합한 컴파일 체계도 제안하였다. 제안한 조건실행은 절전모드를 사용함에 따라 전력을 아낄 수 있지만 기존의 컴파일방식으로는 여러 조건문을 병렬적으로 수행하도록 컴파일할 수 없는 문제가 생긴다. 따라서 본 논문에서는 이런 문제를 밝히고 조건문들을 서로 다른 연산유닛에 할당함으로써 문제를 해결하는 방식을 제안하고 있다. 제안한 방식을 사용할 경우 단순하고 직관적인 방법에 비하여 평균적으로 2.21배의 높은 성능을 얻을 수 있었다.Coarse-Grained Reconfigurable Architecture (CGRA) is one of viable solutions in embedded systems to accelerate data-intensive applications. It typically consists of an array of processing elements (PEs) and a centralized controller, which can provide high performance, flexibility, and low power. Parallel array processing reduces execution time of applications, reconfigurability of PEs allows changing its functionality, and simplified control structure with static scheduling for instruction fetching and data communication minimizes power consumption. However, as applications become complex so that data-intensive parts are having control flows in them, CGRAs face a challenge for its effectiveness. Since the entire PEs are controlled by a centralized unit, it is impossible to execute programs having control divergence among PEs. To overcome the problem, we can adopt the technique called predicated execution, which is the unique solution known so far, but conventional predication techniques have a negative impact on both performance and power consumption due to longer instruction words and unnecessary instruction-fetching/decoding/nullifying steps. Thus, this thesis reveals performance and power issues in predicated execution when a CGRA executes both data- and control-intensive applications, which have not been well-addressed yet. Then it proposes high-performance and low-power predication mechanisms. Experiments conducted through gate-level simulation show that the proposed mechanism improves energy-delay product by 11.9%, 14.7%, and 23.8% compared to three conventional techniques. In addition, this thesis also reveals mapping issues when mapping applications on CGRAs using the proposed predication. A power-saving mode introduced into PEs prohibits multiple conditionals from being parallelized if conventional mapping algorithms are used. Thus, this thesis proposes the framework to release this problem by mapping conditionals to different PEs. Experiments show that mapping results from the proposed approach lead to 2.21 times higher performance than those of the naïve approach.Abstract i Chapter 1 Introduction 1 Chapter 2 Background and Related Work 5 2.1 Coarse-Grained Reconfigurable Architecture . . . . . . . . . . . . 5 2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Target Domain . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.3 Comparison with Other Architectures . . . . . . . . . . . 6 2.1.4 Application Mapping . . . . . . . . . . . . . . . . . . . . . 8 2.1.5 Target CGRA . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Predicated Execution Technique . . . . . . . . . . . . . . . . . . 11 2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Classification . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.3 Different Roles in ILP and DLP processors . . . . . . . . 13 2.2.4 Predication Support on CGRAs . . . . . . . . . . . . . . . 14 Chapter 3 Conventional Predicated Execution Techniques 15 3.1 Partial Predication (Partial) . . . . . . . . . . . . . . . . . . . . 16 3.2 Condition-Based Full Predication (CondFull) . . . . . . . . . . 18 Chapter 4 State-Based Full Predication 23 4.1 Previous Approach (PseudoBranch) . . . . . . . . . . . . . . . 24 4.2 Counter-Based Approach (StateFull) . . . . . . . . . . . . . . 25 4.3 Dual-Issue-Single-Execution (DISE) . . . . . . . . . . . . . . . . 28 4.4 Hybrid Predication . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.4.2 StateFull+Partial . . . . . . . . . . . . . . . . . . . . 34 4.4.3 StateFull+Partial+DISE . . . . . . . . . . . . . . . . 35 Chapter 5 Evaluation 39 5.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.1.1 Conventional Techniques . . . . . . . . . . . . . . . . . . . 39 5.1.2 Proposed Techniques . . . . . . . . . . . . . . . . . . . . . 40 5.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.3.1 Effect of Predication Mechanism on Power Consumption of a PE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.3.2 Quantitative Definitions of short-if and long-if . . . . . . 48 5.3.3 Compilation Strategy in StateFull+Partial . . . . . . 48 5.3.4 Conventional Techniques (Partial, CondFull, and PseudoBranch) vs. Proposed StateFull Technique . . . . . 49 5.3.5 Proposed Hybrid Predication Techniques . . . . . . . . . 53 5.3.6 Putting Together . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.7 Speedup of Applications . . . . . . . . . . . . . . . . . . . 57 Chapter 6 Mapping Framework 61 6.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.2 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.2.1 Overall Flow . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.2.2 From IR to CDFG . . . . . . . . . . . . . . . . . . . . . . 64 6.2.3 Separation . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.2.4 CDFG Mapping . . . . . . . . . . . . . . . . . . . . . . . 68 6.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . 69 6.4.2 Verification of Mapping Framework . . . . . . . . . . . . . 70 6.4.3 Quality of Mapping Results . . . . . . . . . . . . . . . . . 70 Chapter 7 Conclusion 73 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 7.2 Applicable Scope and Future Work . . . . . . . . . . . . . . . . . 75 Appendix 77 국문초록 93 감사의 글 95Docto

Libra: Achieving Efficient Instruction- and Data- Parallel Execution for Mobile Applications.

Mobile computing as exemplified by the smart phone has become an integral part of our daily lives. The next generation of these devices will be driven by providing richer user experiences and compelling capabilities: higher definition multimedia, 3D graphics, augmented reality, and voice interfaces. To meet these goals, the core computing capabilities of the smart phone must be scaled. But, the energy budgets are increasing at a much lower rate, thus fundamental improvements in computing efficiency must be garnered. To meet this challenge, computer architects employ hardware accelerators in the form of SIMD and VLIW. Single-instruction multiple-data (SIMD) accelerators provide high degrees of scalability for applications rich in data-level parallelism (DLP). Very long instruction word (VLIW) accelerators provide moderate scalability for applications with high degrees of instruction-level parallelism (ILP). Unfortunately, applications are not so nicely partitioned into two groups: many applications have some DLP, but also contain significant fractions of code with low trip count loops, complex control/data dependences, or non-uniform execution behavior for which no DLP exists. Therefore, a more adaptive accelerator is required to be able to deploy resources as needed: exploit DLP on SIMD when it’s available, but fall back to ILP on the same hardware when necessary. In this thesis, we first focus on various compiler solutions that solve inefficiency problem in both VLIW and SIMD accelerators. For SIMD accelerators, a new vectorization pass, called SIMD Defragmenter, is introduced to uncover hidden DLP using subgraph identification in SIMD accelerators. CGRA express effectively accelerates sequential code regions using a bypass network in VLIW accelerators, and Resource Recycling leverages stream-graph modulo scheduling technique for scheduling of multiple code regions in multi-core accelerators. Second, we propose the new scalable multicore accelerator referred to as Libra for mobile systems, which can support execution of code regions having both DLP and ILP, as well as hybrid combinations of the two. We believe that as industry requires higher performance, the proposed flexible accelerator and compiler support will put more resources to work in order to meet the performance and power efficiency requirements.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99840/1/yjunpark_1.pd