Automation scripts for generic synthesis and co-simulation for Xronos generated HDLs

With growing developments in applications such as multimedia, technologies has pushed boundaries in the demands and popularity which used for a wide range of application domains. However, as complexity of development process of these applications that involve time-consuming and error-prone tasks increases, it is often scaled-up with development of the devices on the market to supports these applications. ORCC has helped in reducing the complexity of application developments by providing a set of modern tools based on dataflow programming. However, though Xronos generated HDLs has proven to be useful, yet it does not optimized to the resource utilization, power and timing analysis. It also yet to have a wide coverage across High-Level Synthesis (HLS) tools such as Altera Quartus and Synopsys Design Compiler. These coverages include the codes generated can be synthesize with other HDL tools. The critical part of this paper is generating of generic HDL for Altera Quartus or Synopsys Design Compiler from original HDL generated from ORCC and to provide co-simulation, automation environments for the RVC-CAL framework of Altera Quartus’s testbench by extracting simulation data that available from RVC-CAL framework, in which can be used to verify data from both ends (RVC-CAL’s and Quartus’s). Apart from that, in order to fully test generated code, comparison will be made with Xilinx Vivado HLS to benchmark functional test that are made with different HLS tools. This paper present the automation scripts that helps to produce better optimization of resource, power, and timing analysis from Xronos generated HDLs and providing automatic testbench that can be tested with Xilinx Vivado and other HDL tools

Using Efficient Path Profiling to Optimize Memory Consumption of On-Chip Debugging for High-Level Synthesis

High-Level Synthesis (HLS) for FPGAs is attracting popularity and is increasingly used to handle complex systems with multiple integrated components. To increase performance and efficiency, HLS flows now adopt several advanced optimization techniques. Aggressive optimizations and system level integration can cause the introduction of bugs that are only observable on-chip. Debugging support for circuits generated with HLS is receiving a considerable attention. Among the data that can be collected on chip for debugging, one of the most important is the state of the Finite State Machines (FSM) controlling the components of the circuit. However, this usually requires a large amount of memory to trace the behavior during the execution. This work proposes an approach that takes advantage of the HLS information and of the structure of the FSM to compress control flow traces and to integrate optimized components for on-chip debugging. The generated checkers analyze the FSM execution on-fly, automatically notifying when a bug is detected, localizing it and providing data about its cause. The traces are compressed using a software profiling technique, called Efficient Path Profiling (EPP), adapted for the debugging of hardware accelerators generated with HLS. With this technique, the size of the memory used to store control flow traces can be reduced up to 2 orders of magnitude, compared to state-of-the-art

Performance Debugging Frameworks for FPGA High-Level Synthesis

Using high-level synthesis (HLS) tools for field-programmable gate array (FPGA) design is becoming an increasingly popular choice because HLS tools can generate a high-quality design in a short development time. However, current HLS tools still cannot adequately support users in understanding and fixing the performance issues of the current design. That is, current HLS tools lack in performance debugging capability. Previous work on performance debugging automates the process of inserting hardware monitors in low-level register-transfer level (RTL) languages which limits the comprehensibility of the obtained result. Instead, our HLS-based flows offer analysis on a function or loop level and provide more intuitive feedback that can be used to pinpoint the performance bottleneck of a design. In this dissertation, we present a collection of HLS-based debugging frameworks for various purposes and characteristics of the design. First, we address the problem in the HLS synthesis step, where an inaccurate cycle estimation is provided if the program has input-dependent behavior. We propose a new performance estimator that automatically instruments code that models the hardware execution behavior and interprets the information from the HLS software simulation. However, the performance estimation result of this flow may not be accurate for a type of designs that cannot be simulated correctly by existing HLS software simulators. To handle such cases, we propose a new software simulator that provides cycle-accurate result based on the HLS scheduling information. If the input dataset is not available for software simulation or high-level models do not exist for all components of the FPGA design, we also present an on-board monitoring flow for automated cycle extraction and stall analysis. Finally, we address the needs of HLS programmers to automatically find the best set of directives for FPGA designs. We propose a design space exploration (DSE) framework to optimize applications with variable loop bounds in Polybench benchmark. A quantitative comparison among the proposed frameworks is shown using the sparse matrix-vector multiplication benchmark

SoC-based FPGA architecture for image analysis and other highly demanding applications

Al giorno d'oggi, lo sviluppo di algoritmi si concentra su calcoli efficienti in termini di prestazioni ed efficienza energetica. Tecnologie come il field programmable gate array (FPGA) e il system on chip (SoC) basato su FPGA (FPGA/SoC) hanno dimostrato la loro capacità di accelerare applicazioni di calcolo intensive risparmiando al contempo il consumo energetico, grazie alla loro capacità di elevato parallelismo e riconfigurazione dell'architettura. Attualmente, i cicli di progettazione esistenti per FPGA/SoC sono lunghi, a causa della complessità dell'architettura. Pertanto, per colmare il divario tra le applicazioni e le architetture FPGA/SoC e ottenere un design hardware efficiente per l'analisi delle immagini e altri applicazioni altamente demandanti utilizzando lo strumento di sintesi di alto livello, vengono prese in considerazione due strategie complementari: tecniche ad hoc e stima delle prestazioni. Per quanto riguarda le tecniche ad-hoc, tre applicazioni molto impegnative sono state accelerate attraverso gli strumenti HLS: discriminatore di forme di impulso per i raggi cosmici, classificazione automatica degli insetti e re-ranking per il recupero delle informazioni, sottolineando i vantaggi quando questo tipo di applicazioni viene attraversato da tecniche di compressione durante il targeting dispositivi FPGA/SoC. Inoltre, in questa tesi viene proposto uno stimatore delle prestazioni per l'accelerazione hardware per prevedere efficacemente l'utilizzo delle risorse e la latenza per FPGA/SoC, costruendo un ponte tra l'applicazione e i domini architetturali. Lo strumento integra modelli analitici per la previsione delle prestazioni e un motore design space explorer (DSE) per fornire approfondimenti di alto livello agli sviluppatori di hardware, composto da due motori indipendenti: DSE basato sull'ottimizzazione a singolo obiettivo e DSE basato sull'ottimizzazione evolutiva multiobiettivo.Nowadays, the development of algorithms focuses on performance-efficient and energy-efficient computations. Technologies such as field programmable gate array (FPGA) and system on chip (SoC) based on FPGA (FPGA/SoC) have shown their ability to accelerate intensive computing applications while saving power consumption, owing to their capability of high parallelism and reconfiguration of the architecture. Currently, the existing design cycles for FPGA/SoC are time-consuming, owing to the complexity of the architecture. Therefore, to address the gap between applications and FPGA/SoC architectures and to obtain an efficient hardware design for image analysis and highly demanding applications using the high-level synthesis tool, two complementary strategies are considered: ad-hoc techniques and performance estimator. Regarding ad-hoc techniques, three highly demanding applications were accelerated through HLS tools: pulse shape discriminator for cosmic rays, automatic pest classification, and re-ranking for information retrieval, emphasizing the benefits when this type of applications are traversed by compression techniques when targeting FPGA/SoC devices. Furthermore, a comprehensive performance estimator for hardware acceleration is proposed in this thesis to effectively predict the resource utilization and latency for FPGA/SoC, building a bridge between the application and architectural domains. The tool integrates analytical models for performance prediction, and a design space explorer (DSE) engine for providing high-level insights to hardware developers, composed of two independent sub-engines: DSE based on single-objective optimization and DSE based on evolutionary multi-objective optimization