The design, implementation and optimization of FPGA accelerators is a challenging task, especially when the accelerator comprises multiple compute cores distributed across CPU and FPGA resources and memories and exhibits data-dependent runtime behavior.
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We show that Twill provides a sig- nificant performance increase on the CHStone benchmarks with an average 1.63 times increase over the pure hardware approach and an increase of 22.2 times on average over the pure software approach while reducing the area required by the reconfigurable logic by on average 1.73 times compared to the pure hardware approach.
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Twill can extract long-running threads from single threaded C code and distribute these threads across the hardware and software domains to more fully utilize the asymmetric characteristics between processors and the embedded reconfigurable logic fabric. We propose to bridge this gap with Twill, a truly automatic hybrid compiler that can take advantage of the parallelism inherent in these platforms. Unfortunately, the de- velopment tools accompanying these products leave much to be desired, requiring knowledge of both traditional embedded systems languages like C and hardware description languages like Verilog. Increasingly System-On-A-Chip platforms which incorporate both micropro- cessors and re-programmable logic are being utilized across several fields ranging from the automotive industry to network infrastructure. The solutions generated by TSSA are rated as better by both the cost model for the TSSA algorithm and the cost model for the Genetic algorithm while producing low queue counts. The Genetic, TSSA, and Twill’s original partitioning algorithm are all scored against each other’s cost models as well, combining the fitness and performance cost models with queue counts to evaluate each partitioning algorithm. These high communication costs can end up damaging the heterogeneous solution’s performance. Along with the algorithms cost models, one key attribute of interest is queue counts generated, as the more cuts between hardware and software requires queues to pass the data between partition crossings. These algorithms are implemented inside Twill and test bench input code from the CHStone HLS Benchmark tests is used as stimulus. Twill’s original partitioning algorithm is chosen along with two other partitioning algorithms: Tabu Search + Simulated Annealing (TSSA) and Genetic Search (GS). The platform used to implement the algorithms is Cal Poly’s own Twill compiler, created by Doug Gallatin last year. Both estimated outcomes and actual outcomes for the solutions generated are studied and scored.
The purpose of this thesis is to implement various partitioning algorithms onto the same automatic heterogeneous compiler platform to create an apples to apples comparison for AHC partitioning algorithms. We show the interaction between the three components of efficiency and show how bottlenecks are revealed.Īutomatic Heterogeneous Compilers allows blended hardware-software solutions to be explored without the cost of a full-fledged design team, but limited research exists on current partitioning algorithms responsible for separating hardware and software. The proposed methodology is applied on a number of use cases to illustrate the methodology. We propose a taxonomy of possible causes and practical methods to identify and quantify the overheads. After quantification of the efficiencies, a detailed analysis has to reveal the reasons for the lost frequencies, lost area and lost cycles. A formal approach is proposed to decompose the efficiency into three components: frequency, area and cycles. The analysis of the difference between actual and ideal runtime reveals the overheads and bottlenecks. The efficiency of runtime performance is defined with respect to the ideal computational runtime in absence of inefficiencies.
Most work on High-Performance Computing (HPC) for FPGAs only studies runtime performance or cost, while we are interested in how far we are from peak performance and, more importantly, why.
We propose a methodology to study and to quantify efficiency and the impact of overheads on runtime performance.
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How To Beat Every Poly Bridge 2 Level Under Budget and Unbreaking! (World 5). Poly Bridge 2 Gameplay | 4-15 : Twists and Turns. Poly Bridge 2 4-05: Triple Decker Drawbridge.
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