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

Document Classification Systems in Heterogeneous Computing Environments

Datacenter workloads demand high throughput, low cost and power efficient solutions. In most data centers the operating costs dominates the infrastructure cost. The ever growing amounts of data and the critical need for higher throughput, more energy efficient document classification solutions motivated us to investigate alternatives to the traditional homogeneous CPU based implementations of document classification systems. Several heterogeneous systems were investigated in the past where CPUs were combined with GPUs and FPGAs as system accelerators. The increasing complexity of FPGAs made them an interesting device in the heterogeneous computing environments and on the other hand difficult to program using Hardware Description languages. We explore the trade-offs when using high level synthesis and low level synthesis when programming FPGAs. Using low level synthesis results in less hardware resource usage on FPGAs and also offers the higher throughput compared to using HLS tool. While using HLS tool different heterogeneous computing devices such as multicore CPU and GPU targeted. Through our implementation experience and empirical results for data centric applications, we conclude that we can achieve power efficient results for these set of applications by either using low level synthesis or high level synthesis for programming FPGAs

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Translating Timing into an Architecture: The Synergy of COTSon and HLS (Domain Expertise: Designing a Computer Architecture via HLS)

Translating a system requirement into a low-level representation (e.g., register transfer level or RTL) is the typical goal of the design of FPGA-based systems. However, the Design Space Exploration (DSE) needed to identify the final architecture may be time consuming, even when using high-level synthesis (HLS) tools. In this article, we illustrate our hybrid methodology, which uses a frontend for HLS so that the DSE is performed more rapidly by using a higher level abstraction, but without losing accuracy, thanks to the HP-Labs COTSon simulation infrastructure in combination with our DSE tools (MYDSE tools). In particular, this proposed methodology proved useful to achieve an appropriate design of a whole system in a shorter time than trying to design everything directly in HLS. Our motivating problem was to deploy a novel execution model called data-flow threads (DF-Threads) running on yet-to-be-designed hardware. For that goal, directly using the HLS was too premature in the design cycle. Therefore, a key point of our methodology consists in defining the first prototype in our simulation framework and gradually migrating the design into the Xilinx HLS after validating the key performance metrics of our novel system in the simulator. To explain this workflow, we first use a simple driving example consisting in the modelling of a two-way associative cache. Then, we explain how we generalized this methodology and describe the types of results that we were able to analyze in the AXIOM project, which helped us reduce the development time from months/weeks to days/hours

Slack Exploitation for Aggressive Dynamic Power Reduction in SoC

The increasing power consumption of today’s system-on-chip (SoC) outpaces the trend of increasing battery capacity. The applications offered to customers grow tremendously too, a trend that is accelerating in the future. This yields stronger requirements for lower power consumption. During design, a system is dimensioned to worst-case workload requirements. Most of the time, workload is far below this level, which results in slack in some parts of the system. Our idea is to exploit this available slack by using adequate variants of dynamic voltage and frequency scaling and power gating. For scalability reasons, we commence our research with local dynamic adaptive power and frequency scaling, based on the slack observed at run time. This paper presents the motivations and possible directions for our research

The hArtes Tool Chain

This chapter describes the different design steps needed to go from legacy code to a transformed application that can be efficiently mapped on the hArtes platform

Design and resource management of reconfigurable multiprocessors for data-parallel applications

FPGA (Field-Programmable Gate Array)-based custom reconfigurable computing machines have established themselves as low-cost and low-risk alternatives to ASIC (Application-Specific Integrated Circuit) implementations and general-purpose microprocessors in accelerating a wide range of computation-intensive applications. Most often they are Application Specific Programmable Circuiits (ASPCs), which are developer programmable instead of user programmable. The major disadvantages of ASPCs are minimal programmability, and significant time and energy overheads caused by required hardware reconfiguration when the problem size outnumbers the available reconfigurable resources; these problems are expected to become more serious with increases in the FPGA chip size. On the other hand, dominant high-performance computing systems, such as PC clusters and SMPs (Symmetric Multiprocessors), suffer from high communication latencies and/or scalability problems. This research introduces low-cost, user-programmable and reconfigurable MultiProcessor-on-a-Programmable-Chip (MPoPC) systems for high-performance, low-cost computing. It also proposes a relevant resource management framework that deals with performance, power consumption and energy issues. These semi-customized systems reduce significantly runtime device reconfiguration by employing userprogrammable processing elements that are reusable for different tasks in large, complex applications. For the sake of illustration, two different types of MPoPCs with hardware FPUs (floating-point units) are designed and implemented for credible performance evaluation and modeling: the coarse-grain MIMD (Multiple-Instruction, Multiple-Data) CG-MPoPC machine based on a processor IP (Intellectual Property) core and the mixed-mode (MIMD, SIMD or M-SIMD) variant-grain HERA (HEterogeneous Reconfigurable Architecture) machine. In addition to alleviating the above difficulties, MPoPCs can offer several performance and energy advantages to our data-parallel applications when compared to ASPCs; they are simpler and more scalable, and have less verification time and cost. Various common computation-intensive benchmark algorithms, such as matrix-matrix multiplication (MMM) and LU factorization, are studied and their parallel solutions are shown for the two MPoPCs. The performance is evaluated with large sparse real-world matrices primarily from power engineering. We expect even further performance gains on MPoPCs in the near future by employing ever improving FPGAs. The innovative nature of this work has the potential to guide research in this arising field of high-performance, low-cost reconfigurable computing. The largest advantage of reconfigurable logic lies in its large degree of hardware customization and reconfiguration which allows reusing the resources to match the computation and communication needs of applications. Therefore, a major effort in the presented design methodology for mixed-mode MPoPCs, like HERA, is devoted to effective resource management. A two-phase approach is applied. A mixed-mode weighted Task Flow Graph (w-TFG) is first constructed for any given application, where tasks are classified according to their most appropriate computing mode (e.g., SIMD or MIMD). At compile time, an architecture is customized and synthesized for the TFG using an Integer Linear Programming (ILP) formulation and a parameterized hardware component library. Various run-time scheduling schemes with different performanceenergy objectives are proposed. A system-level energy model for HERA, which is based on low-level implementation data and run-time statistics, is proposed to guide performance-energy trade-off decisions. A parallel power flow analysis technique based on Newton\u27s method is proposed and employed to verify the methodology

Optimising and evaluating designs for reconfigurable hardware

Growing demand for computational performance, and the rising cost for chip design and manufacturing make reconfigurable hardware increasingly attractive for digital system implementation. Reconfigurable hardware, such as field-programmable gate arrays (FPGAs), can deliver performance through parallelism while also providing flexibility to enable application builders to reconfigure them. However, reconfigurable systems, particularly those involving run-time reconfiguration, are often developed in an ad-hoc manner. Such an approach usually results in low designer productivity and can lead to inefficient designs. This thesis covers three main achievements that address this situation. The first achievement is a model that captures design parameters of reconfigurable hardware and performance parameters of a given application domain. This model supports optimisations for several design metrics such as performance, area, and power consumption. The second achievement is a technique that enhances the relocatability of bitstreams for reconfigurable devices, taking into account heterogeneous resources. This method increases the flexibility of modules represented by these bitstreams while reducing configuration storage size and design compilation time. The third achievement is a technique to characterise the power consumption of FPGAs in different activity modes. This technique includes the evaluation of standby power and dedicated low-power modes, which are crucial in meeting the requirements for battery-based mobile devices