1,011 research outputs found
Application-Specific Number Representation
Reconfigurable devices, such as Field Programmable Gate Arrays (FPGAs), enable application-
specific number representations. Well-known number formats include fixed-point, floating-
point, logarithmic number system (LNS), and residue number system (RNS). Such different
number representations lead to different arithmetic designs and error behaviours, thus produc-
ing implementations with different performance, accuracy, and cost.
To investigate the design options in number representations, the first part of this thesis presents
a platform that enables automated exploration of the number representation design space. The
second part of the thesis shows case studies that optimise the designs for area, latency or
throughput from the perspective of number representations.
Automated design space exploration in the first part addresses the following two major issues:
² Automation requires arithmetic unit generation. This thesis provides optimised
arithmetic library generators for logarithmic and residue arithmetic units, which support
a wide range of bit widths and achieve significant improvement over previous designs.
² Generation of arithmetic units requires specifying the bit widths for each
variable. This thesis describes an automatic bit-width optimisation tool called R-Tool,
which combines dynamic and static analysis methods, and supports different number
systems (fixed-point, floating-point, and LNS numbers).
Putting it all together, the second part explores the effects of application-specific number
representation on practical benchmarks, such as radiative Monte Carlo simulation, and seismic
imaging computations. Experimental results show that customising the number representations
brings benefits to hardware implementations: by selecting a more appropriate number format,
we can reduce the area cost by up to 73.5% and improve the throughput by 14.2% to 34.1%; by
performing the bit-width optimisation, we can further reduce the area cost by 9.7% to 17.3%.
On the performance side, hardware implementations with customised number formats achieve
5 to potentially over 40 times speedup over software implementations
Arithmetic core generation using bit heaps
International audienceA bit heap is a data structure that holds the unevaluated sum of an arbitrary number of bits, each weighted by some power of two. Most advanced arithmetic cores can be viewed as involving one or several bit heaps. We claim here that this point of view leads to better global optimization at the algebraic level, at the circuit level, and in terms of software engineering. To demonstrate it, a generic software framework is introduced for the definition and optimization of bit heaps. This framework, targeting DSP-enabled FPGAs, is developed within the open-source FloPoCo arithmetic core generator. Its versatility is demonstrated on several examples: multipliers, complex multipliers, polynomials, and discrete cosine transform
Floating-point exponential functions for DSP-enabled FPGAs
International audienceThis article presents a floating-point exponential operator generator targeting recent FPGAs with embedded memories and DSP blocks. A single-precision operator consumes just one DSP block, 18Kbits of dual-port memory, and 392 slices on Virtex-4. For larger precisions, a generic approach based on polynomial approximation is used and proves more resource-efficient than the literature. For instance a double-precision operator consumes 5 BlockRAM and 12 DSP48 blocks on Virtex-5, or 10 M9k and 22 18x18 multipliers on Stratix III. This approach is flexible, scales well beyond double-precision, and enables frequencies close to the FPGA's nominal frequency. All the proposed architectures are last-bit accurate for all the floating-point range.They are available in the open-source FloPoCo framework
HPIPE: Heterogeneous Layer-Pipelined and Sparse-Aware CNN Inference for FPGAs
We present both a novel Convolutional Neural Network (CNN) accelerator
architecture and a network compiler for FPGAs that outperforms all prior work.
Instead of having generic processing elements that together process one layer
at a time, our network compiler statically partitions available device
resources and builds custom-tailored hardware for each layer of a CNN. By
building hardware for each layer we can pack our controllers into fewer lookup
tables and use dedicated routing. These efficiencies enable our accelerator to
utilize 2x the DSPs and operate at more than 2x the frequency of prior work on
sparse CNN acceleration on FPGAs. We evaluate the performance of our
architecture on both sparse Resnet-50 and dense MobileNet Imagenet classifiers
on a Stratix 10 2800 FPGA. We find that the sparse Resnet-50 model has
throughput at a batch size of 1 of 4550 images/s, which is nearly 4x the
throughput of NVIDIA's fastest machine learning targeted GPU, the V100, and
outperforms all prior work on FPGAs.Comment: 8 Pages, 11 Figure
Some Optimizations of Hardware Multiplication by Constant Matrices
International audienceThis paper presents some improvements on the optimization of hardware multiplication by constant matrices. We focus on the automatic generation of circuits that involve constant matrix multiplication, i.e. multiplication of a vector by a constant matrix. The proposed method, based on number recoding and dedicated common sub-expression factorization algorithms was implemented in a VHDL generator. Our algorithms and generator have been extended to the case of some digital filters based on multiplication by a constant matrix and delay operations. The obtained results on several applications have been implemented on FPGAs and compared to previous solutions. Up to 40% area and speed savings are achieved
Pipelining Saturated Accumulation
Aggressive pipelining and spatial parallelism allow integrated circuits (e.g., custom VLSI, ASICs, and FPGAs) to achieve high throughput on many Digital Signal Processing applications. However, cyclic data dependencies in the computation can limit parallelism and reduce the efficiency and speed of an implementation. Saturated accumulation is an important example where such a cycle limits the throughput of signal processing applications. We show how to reformulate saturated addition as an associative operation so that we can use a parallel-prefix calculation to perform saturated accumulation at any data rate supported by the device. This allows us, for example, to design a 16-bit saturated accumulator which can operate at 280 MHz on a Xilinx Spartan-3(XC3S-5000-4) FPGA, the maximum frequency supported by the component's DCM
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
Applications for FPGA's on Nanosatellites
This thesis examines the feasibility of using a Field Programmable Gate Array (FPGA) based design on-board a CubeSat-sized nanosatellite. FPGAs are programmable logic devices that allow for the implementation of custom digital hardware on a single Integrated Circuit (IC). By using these FPGAs in spacecraft, more efficient processing can be done by moving the design onto hardware. A variety of different FPGA-based designs are looked at, including a Watchdog Timer (WDT), a Global Positioning System (GPS) receiver, and a camera interface
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