Optimisations arithmétiques et synthèse de haut niveau

High-level synthesis (HLS) tools offer increased productivity regarding FPGA programming.However, due to their relatively young nature, they still lack many arithmetic optimizations.This thesis proposes safe arithmetic optimizations that should always be applied.These optimizations are simple operator specializations, following the C semantic.Other require to a lift the semantic embedded in high-level input program languages, which are inherited from software programming, for an improved accuracy/cost/performance ratio.To demonstrate this claim, the sum-of-product of floating-point numbers is used as a case study. The sum is performed on a fixed-point format, which is tailored to the application, according to the context in which the operator is instantiated.In some cases, there is not enough information about the input data to tailor the fixed-point accumulator.The fall-back strategy used in this thesis is to generate an accumulator covering the entire floating-point range.This thesis explores different strategies for implementing such a large accumulator, including new ones.The use of a 2's complement representation instead of a sign+magnitude is demonstrated to save resources and to reduce the accumulation loop delay.Based on a tapered precision scheme and an exact accumulator, the posit number systems claims to be a candidate to replace the IEEE floating-point format.A throughout analysis of posit operators is performed, using the same level of hardware optimization as state-of-the-art floating-point operators.Their cost remains much higher that their floating-point counterparts in terms of resource usage and performance. Finally, this thesis presents a compatibility layer for HLS tools that allows one code to be deployed on multiple tools.This library implements a strongly typed custom size integer type along side a set of optimized custom operators.À cause de la nature relativement jeune des outils de synthèse de haut-niveau (HLS), de nombreuses optimisations arithmétiques n'y sont pas encore implémentées. Cette thèse propose des optimisations arithmétiques se servant du contexte spécifique dans lequel les opérateurs sont instanciés.Certaines optimisations sont de simples spécialisations d'opérateurs, respectant la sémantique du C.D'autres nécéssitent de s'éloigner de cette sémantique pour améliorer le compromis précision/coût/performance.Cette proposition est démontré sur des sommes de produits de nombres flottants.La somme est réalisée dans un format en virgule-fixe défini par son contexte.Quand trop peu d’informations sont disponibles pour définir ce format en virgule-fixe, une stratégie est de générer un accumulateur couvrant l'intégralité du format flottant.Cette thèse explore plusieurs implémentations d'un tel accumulateur.L'utilisation d'une représentation en complément à deux permet de réduire le chemin critique de la boucle d'accumulation, ainsi que la quantité de ressources utilisées. Un format alternatif aux nombres flottants, appelé posit, propose d'utiliser un encodage à précision variable.De plus, ce format est augmenté par un accumulateur exact.Pour évaluer précisément le coût matériel de ce format, cette thèse présente des architectures d'opérateurs posits, implémentés avec le même degré d'optimisation que celui de l'état de l'art des opérateurs flottants.Une analyse détaillée montre que le coût des opérateurs posits est malgré tout bien plus élevé que celui de leurs équivalents flottants.Enfin, cette thèse présente une couche de compatibilité entre outils de HLS, permettant de viser plusieurs outils avec un seul code. Cette bibliothèque implémente un type d'entiers de taille variable, avec de plus une sémantique strictement typée, ainsi qu'un ensemble d'opérateurs ad-hoc optimisés

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

フロアプラン指向高位合成手法とイジング計算機応用に関する研究

早大学位記番号:新7790早稲田大

Closing the Gap between FPGA and ASIC:Balancing Flexibility and Efficiency

Despite many advantages of Field-Programmable Gate Arrays (FPGAs), they fail to take over the IC design market from Application-Specific Integrated Circuits (ASICs) for high-volume and even medium-volume applications, as FPGAs come with significant cost in area, delay, and power consumption. There are two main reasons that FPGAs have huge efficiency gap with ASICs: (1) FPGAs are extremely flexible as they have fully programmable soft-logic blocks and routing networks, and (2) FPGAs have hard-logic blocks that are only usable by a subset of applications. In other words, current FPGAs have a heterogeneous structure comprised of the flexible soft-logic and the efficient hard-logic blocks that suffer from inefficiency and inflexibility, respectively. The inefficiency of the soft-logic is a challenge for any application that is mapped to FPGAs, and lack of flexibility in the hard-logic results in a waste of resources when an application cannot use the hard-logic. In this thesis, we approach the inefficiency problem of FPGAs by bridging the efficiency/flexibility gap of the hard- and soft-logic. The main goal of this thesis is to compromise on efficiency of the hard-logic for flexibility, on the one hand, and to compromise on flexibility of the soft-logic for efficiency, on the other hand. In other words, this thesis deals with two issues: (1) adding more generality to the hard-logic of FPGAs, and (2) improving the soft-logic by adapting it to the generic requirements of applications. In the first part of the thesis, we introduce new techniques that expand the functionality of FPGAs hard-logic. The hard-logic includes the dedicated resources that are tightly coupled with the soft-logic –i.e., adder circuitry and carry chains –as well as the stand-alone ones –i.e., DSP blocks. These specialized resources are intended to accelerate critical arithmetic operations that appear in the pre-synthesis representation of applications; we introduce mapping and architectural solutions, which enable both types of the hard-logic to support additional arithmetic operations. We first present a mapping technique that extends the application of FPGAs carry chains for carry-save arithmetic, and then to increase the generality of the hard-logic, we introduce novel architectures; using these architectures, more applications can take advantage of FPGAs hard-logic. In the second part of the thesis, we improve the efficiency of FPGAs soft-logic by exploiting the circuit patterns that emerge after logic synthesis, i.e., connection and logic patterns. Using these patterns, we design new soft-logic blocks that have less flexibility, but more efficiency than current ones. In this part, we first introduce logic chains, fixed connections that are integrated between the soft-logic blocks of FPGAs and are well-suited for long chains of logic that appear post-synthesis. Logic chains provide fast and low cost connectivity, increase the bandwidth of the logic blocks without changing their interface with the routing network, and improve the logic density of soft-logic blocks. In addition to logic chains and as a complementary contribution, we present a non-LUT soft-logic block that comprises simple and pre-connected cells. The structure of this logic block is inspired from the logic patterns that appear post-synthesis. This block has a complexity that is only linear in the number of inputs, it sports the potential for multiple independent outputs, and the delay is only logarithmic in the number of inputs. Although this new block is less flexible than a LUT, we show (1) that effective mapping algorithms exist, (2) that, due to their simplicity, poor utilization is less of an issue than with LUTs, and (3) that a few LUTs can still be used in extreme unfortunate cases. In summary, to bridge the gap between FPGAs and ASICs, we approach the problem from two complementary directions, which balance flexibility and efficiency of the logic blocks of FPGAs. However, we were able to explore a few design points in this thesis, and future work could focus on further exploration of the design space

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing