Fonctions élémentaires en virgule flottante pour les accélérateurs reconfigurables

National audienceLes circuits reconfigurables FPGA ont désormais une capacité telle qu'ils peuvent être utilisés à des tâches d'accélération de calcul en virgule flottante. La littérature (et depuis peu les constructeurs) proposent des opérateurs pour les quatre opérations. L'étape suivante est de proposer des opérateurs pour les fonctions élémentaires les plus utilisées. Parmi celles-ci, nous proposons des architectures dédiées pour l'évaluation des fonctions exponentielles, logarithme, sinus et cosinus, et étudions les compromis possibles. Pour chacune de ces fonctions, un seul de ces opérateurs surpasse d'un facteur dix les processeurs généralistes en terme de débit, tout en occupant une fraction des ressources matérielles du FPGA. Tous ces opérateurs sont disponibles librement sur http://www.ens-lyon.fr/LIP/Arenaire/

Rethinking FPGA Architectures for Deep Neural Network applications

The prominence of machine learning-powered solutions instituted an unprecedented trend of integration into virtually all applications with a broad range of deployment constraints from tiny embedded systems to large-scale warehouse computing machines. While recent research confirms the edges of using contemporary FPGAs to deploy or accelerate machine learning applications, especially where the latency and energy consumption are strictly limited, their pre-machine learning optimised architectures remain a barrier to the overall efficiency and performance. Realizing this shortcoming, this thesis demonstrates an architectural study aiming at solutions that enable hidden potentials in the FPGA technology, primarily for machine learning algorithms. Particularly, it shows how slight alterations to the state-of-the-art architectures could significantly enhance the FPGAs toward becoming more machine learning-friendly while maintaining the near-promised performance for the rest of the applications. Eventually, it presents a novel systematic approach to deriving new block architectures guided by designing limitations and machine learning algorithm characteristics through benchmarking. First, through three modifications to Xilinx DSP48E2 blocks, an enhanced digital signal processing (DSP) block for important computations in embedded deep neural network (DNN) accelerators is described. Then, two tiers of modifications to FPGA logic cell architecture are explained that deliver a variety of performance and utilisation benefits with only minor area overheads. Eventually, with the goal of exploring this new design space in a methodical manner, a problem formulation involving computing nested loops over multiply-accumulate (MAC) operations is first proposed. A quantitative methodology for deriving efficient coarse-grained compute block architectures from benchmarks is then suggested together with a family of new embedded blocks, called MLBlocks

Класифікація та архітектурні особливості програмованих мультипроцесорних систем-на-кристалі

Provided general information on embedded multiprocessor systems-on-chip based on FPGA (FPGA-MPSoC). Completed a comprehensive analysis of the architectural features and provided Shih rock classification FPGA-MPSoC. Powered overview of recent research in the development of FPGA-MPSoC. A wide circle of such systems in order to study trends in architecture and all problems solvedПредоставлено общую информацию о встроенных мультипроцессорных систем-на-кристалле на базе ПЛИС (FPGA-MPSoC). Выполнено всесторонний анализ архитектурных особенностей и предоставлена ​​широкая классификация FPGA-MPSoC. Приведены обзор последних исследований в области разработки FPGA-MPSoC. Представлен широкий круг таких систем с целью исследования всех тенденциях архитектуры и решаемых задачПредоставлено общую информацию о встроенных мультипроцессорных систем-на-кристалле на базе ПЛИС (FPGA-MPSoC). Выполнено всесторонний анализ архитектурных особенностей и предоставлена ​​широкая классификация FPGA-MPSoC. Приведены обзор последних исследований в области разработки FPGA-MPSoC. Представлен широкий круг таких систем с целью исследования всех тенденциях архитектуры и решаемых зада

Combining dynamic and static scheduling in high-level synthesis

Field Programmable Gate Arrays (FPGAs) are starting to become mainstream devices for custom computing, particularly deployed in data centres. However, using these FPGA devices requires familiarity with digital design at a low abstraction level. In order to enable software engineers without a hardware background to design custom hardware, high-level synthesis (HLS) tools automatically transform a high-level program, for example in C/C++, into a low-level hardware description. A central task in HLS is scheduling: the allocation of operations to clock cycles. The classic approach to scheduling is static, in which each operation is mapped to a clock cycle at compile time, but recent years have seen the emergence of dynamic scheduling, in which an operation’s clock cycle is only determined at run-time. Both approaches have their merits: static scheduling can lead to simpler circuitry and more resource sharing, while dynamic scheduling can lead to faster hardware when the computation has a non-trivial control flow. This thesis proposes a scheduling approach that combines the best of both worlds. My idea is to use existing program analysis techniques in software designs, such as probabilistic analysis and formal verification, to optimize the HLS hardware. First, this thesis proposes a tool named DASS that uses a heuristic-based approach to identify the code regions in the input program that are amenable to static scheduling and synthesises them into statically scheduled components, also known as static islands, leaving the top-level hardware dynamically scheduled. Second, this thesis addresses a problem of this approach: that the analysis of static islands and their dynamically scheduled surroundings are separate, where one treats the other as black boxes. We apply static analysis including dependence analysis between static islands and their dynamically scheduled surroundings to optimize the offsets of static islands for high performance. We also apply probabilistic analysis to estimate the performance of the dynamically scheduled part and use this information to optimize the static islands for high area efficiency. Finally, this thesis addresses the problem of conservatism in using sequential control flow designs which can limit the throughput of the hardware. We show this challenge can be solved by formally proving that certain control flows can be safely parallelised for high performance. This thesis demonstrates how to use automated formal verification to find out-of-order loop pipelining solutions and multi-threading solutions from a sequential program.Open Acces

A unified hardware/software runtime environment for FPGA-based reconfigurable computers using BORPH

Fulltext linkThis paper explores the design and implementation of BORPH, an operating system designed for FPGA-based reconfigurable computers. Hardware designs execute as normal UNIX processes under BORPH, having access to standard OS services, such as file system support. Hardware and software components of user designs may, therefore, run as communicating processes within BORPH's runtime environment. The familiar language independent UNIX kernel interface facilitates easy design reuse and rapid application development. To develop hardware designs, a Simulink-based design flow that integrates with BORPH is employed. Performances of BORPH on two on-chip systems implemented on a BEE2 platform are compared. © 2008 ACM.link_to_subscribed_fulltex

Exploring Hardware Fault Impacts on Different Real Number Representations of the Structural Resilience of TCUs in GPUs

The most recent generations of graphics processing units (GPUs) boost the execution of convolutional operations required by machine learning applications by resorting to specialized and efficient in-chip accelerators (Tensor Core Units or TCUs) that operate on matrix multiplication tiles. Unfortunately, modern cutting-edge semiconductor technologies are increasingly prone to hardware defects, and the trend to highly stress TCUs during the execution of safety-critical and high-performance computing (HPC) applications increases the likelihood of TCUs producing different kinds of failures. In fact, the intrinsic resiliency to hardware faults of arithmetic units plays a crucial role in safety-critical applications using GPUs (e.g., in automotive, space, and autonomous robotics). Recently, new arithmetic formats have been proposed, particularly those suited to neural network execution. However, the reliability characterization of TCUs supporting different arithmetic formats was still lacking. In this work, we quantitatively assessed the impact of hardware faults in TCU structures while employing two distinct formats (floating-point and posit) and using two different configurations (16 and 32 bits) to represent real numbers. For the experimental evaluation, we resorted to an architectural description of a TCU core (PyOpenTCU) and performed 120 fault simulation campaigns, injecting around 200,000 faults per campaign and requiring around 32 days of computation. Our results demonstrate that the posit format of TCUs is less affected by faults than the floating-point one (by up to three orders of magnitude for 16 bits and up to twenty orders for 32 bits). We also identified the most sensible fault locations (i.e., those that produce the largest errors), thus paving the way to adopting smart hardening solutions

Validation and verification of the interconnection of hardware intellectual property blocks for FPGA-based packet processing systems

As networks become more versatile, the computational requirement for supporting additional functionality increases. The increasing demands of these networks can be met by Field Programmable Gate Arrays (FPGA), which are an increasingly popular technology for implementing packet processing systems. The fine-grained parallelism and density of these devices can be exploited to meet the computational requirements and implement complex systems on a single chip. However, the increasing complexity of FPGA-based systems makes them susceptible to errors and difficult to test and debug. To tackle the complexity of modern designs, system-level languages have been developed to provide abstractions suited to the domain of the target system. Unfortunately, the lack of formality in these languages can give rise to errors that are not caught until late in the design cycle. This thesis presents three techniques for verifying and validating FPGA-based packet processing systems described in a system-level description language. First, a type system is applied to the system description language to detect errors before implementation. Second, system-level transaction monitoring is used to observe high-level events on-chip following implementation. Third, the high-level information embodied in the system description language is exploited to allow the system to be automatically instrumented for on-chip monitoring. This thesis demonstrates that these techniques catch errors which are undetected by traditional verification and validation tools. The locations of faults are specified and errors are caught earlier in the design flow, which saves time by reducing synthesis iterations

Rapid Prototyping and Exploration Environment for Generating C-to-Hardware-Compilers

There is today an ever-increasing demand for more computational power coupled with a desire to minimize energy requirements. Hardware accelerators currently appear to be the best solution to this problem. While general purpose computation with GPUs seem to be very successful in this area, they perform adequately only in those cases where the data access patterns and utilized algorithms fit the underlying architecture. ASICs on the other hand can yield even better results in terms of performance and energy consumption, but are very inflexible, as they are manufactured with an application specific circuitry. Field Programmable Gate Arrays (FPGAs) represent a combination of approaches: With their application specific hardware they provide high computational power while requiring, for many applications, less energy than a CPU or a GPU. On the other hand they are far more flexible than an ASIC due to their reconfigurability. The only remaining problem is the programming of the FPGAs, as they are far more difficult to program compared to regular software. To allow common software developers, who have at best very limited knowledge in hardware design, to make use of these devices, tools were developed that take a regular high level language and generate hardware from it. Among such tools, C-to-HDL compilers are a particularly wide-spread approach. These compilers attempt to translate common C code into a hardware description language from which a datapath is generated. Most of these compilers have many restrictions for the input and differ in their underlying generated micro architecture, their scheduling method, their applied optimizations, their execution model and even their target hardware. Thus, a comparison of a certain aspect alone, like their implemented scheduling method or their generated micro architecture, is almost impossible, as they differ in so many other aspects. This work provides a survey of the existing C-to-HDL compilers and presents a new approach to evaluating and exploring different micro architectures for dynamic scheduling used by such compilers. From a mathematically formulated rule set the Triad compiler generates a backend for the Scale compiler framework, which then implements a hardware generation backend with described dynamic scheduling. While more than a factor of four slower than hardware from highly optimized compilers, this environment allows easy comparison and exploration of different rule sets and the micro architecture for the dynamically scheduled datapaths generated from them. For demonstration purposes a rule set modeling the COCOMA token flow model from the COMRADE 2.0 compiler was implemented. Multiple variants of it were explored: Savings of up to 11% of the required hardware resources were possible

A Field Programmable Gate Array Architecture for Two-Dimensional Partial Reconfiguration

Reconfigurable machines can accelerate many applications by adapting to their needs through hardware reconfiguration. Partial reconfiguration allows the reconfiguration of a portion of a chip while the rest of the chip is busy working on tasks. Operating system models have been proposed for partially reconfigurable machines to handle the scheduling and placement of tasks. They are called OS4RC in this dissertation. The main goal of this research is to address some problems that come from the gap between OS4RC and existing chip architectures and the gap between OS4RC models and practical applications. Some existing OS4RC models are based on an impractical assumption that there is no data exchange channel between IP (Intellectual Property) circuits residing on a Field Programmable Gate Array (FPGA) chip and between an IP circuit and FPGA I/O pins. For models that do not have such an assumption, their inter-IP communication channels have severe drawbacks. Those channels do not work well with 2-D partial reconfiguration. They are not suitable for intensive data stream processing. And frequently they are very complicated to design and very expensive. To address these problems, a new chip architecture that can better support inter-IP and IP-I/O communication is proposed and a corresponding OS4RC kernel is then specified. The proposed FPGA architecture is based on an array of clusters of configurable logic blocks, with each cluster serving as a partial reconfiguration unit, and a mesh of segmented buses that provides inter-IP and IP-I/O communication channels. The proposed OS4RC kernel takes care of the scheduling, placement, and routing of circuits under the constraints of the proposed architecture. Features of the new architecture in turns reduce the kernel execution times and enable the runtime scheduling, placement and routing. The area cost and the configuration memory size of the new chip architecture are calculated and analyzed. And the efficiency of the OS4RC kernel is evaluated via simulation using three different task models