Performance and area evaluations of processor-based benchmarks on FPGA devices

The computing system on SoCs is being long-term research since the FPGA technology has emerged due to its personality of re-programmable fabric, reconfigurable computing, and fast development time to market. During the last decade, uni-processor in a SoC is no longer to deal with the high growing market for complex applications such as Mobile Phones audio and video encoding, image and network processing. Due to the number of transistors on a silicon wafer is increasing, the recent FPGAs or embedded systems are advancing toward multi-processor-based design to meet tremendous performance and benefit this kind of systems are possible. Therefore, is an upcoming age of the MPSoC. In addition, most of the embedded processors are soft-cores, because they are flexible and reconfigurable for specific software functions and easy to build homogenous multi-processor systems for parallel programming. Moreover, behavioural synthesis tools are becoming a lot more powerful and enable to create datapath of logic units from high-level algorithms such as C to HDL and available for partitioning a HW/SW concurrent methodology. A range of embedded processors is able to implement on a FPGA-based prototyping to integrate the CPUs on a programmable device. This research is, firstly represent different types of computer architectures in modern embedded processors that are followed in different type of software applications (eg. Multi-threading Operations or Complex Functions) on FPGA-based SoCs; and secondly investigate their capability by executing a wide-range of multimedia software codes (Integer-algometric only) in different models of the processor-systems (uni-processor or multi-processor or Co-design), and finally compare those results in terms of the benchmarks and resource utilizations within FPGAs. All the examined programs were written in standard C and executed in a variety numbers of soft-core processors or hardware units to obtain the execution times. However, the number of processors and their customizable configuration or hardware datapath being generated are limited by a target FPGA resource, and designers need to understand the FPGA-based tradeoffs that have been considered - Speed versus Area. For this experimental purpose, I defined benchmarks into DLP / HLS catalogues, which are "data" and "function" intensive respectively. The programs of DLP will be executed in LEON3 MP and LE1 CMP multi-processor systems and the programs of HLS in the LegUp Co-design system on target FPGAs. In preliminary, the performance of the soft-core processors will be examined by executing all the benchmarks. The whole story of this thesis work centres on the issue of the execute times or the speed-up and area breakdown on FPGA devices in terms of different programs

A Framework for the Design and Analysis of High-Performance Applications on FPGAs using Partial Reconfiguration

The field-programmable gate array (FPGA) is a dynamically reconfigurable digital logic chip used to implement custom hardware. The large densities of modern FPGAs and the capability of the on-thely reconfiguration has made the FPGA a viable alternative to fixed logic hardware chips such as the ASIC. In high-performance computing, FPGAs are used as co-processors to speed up computationally intensive processes or as autonomous systems that realize a complete hardware application. However, due to the limited capacity of FPGA logic resources, denser FPGAs must be purchased if more logic resources are required to realize all the functions of a complex application. Alternatively, partial reconfiguration (PR) can be used to swap, on demand, idle components of the application with active components. This research uses PR to swap components to improve the performance of the application given the limited logic resources available with smaller but economical FPGAs. The swap is called ”resource sharing PR”. In a pipelined design of multiple hardware modules (pipeline stages), resource sharing PR is a technique that uses PR to improve the performance of pipeline bottlenecks. This is done by reconfiguring other pipeline stages, typically those that are idle waiting for data from a bottleneck, into an additional parallel bottleneck module. The target pipeline of this research is a two-stage “slow-toast” pipeline where the flow of data traversing the pipeline transitions from a relatively slow, bottleneck stage to a fast stage. A two stage pipeline that combines FPGA-based hardware implementations of well-known Bioinformatics search algorithms, the X! Tandem algorithm and the Smith-Waterman algorithm, is implemented for this research; the implemented pipeline demonstrates that characteristics of these algorithm. The experimental results show that, in a database of unknown peptide spectra, when matching spectra with 388 peaks or greater, performing resource sharing PR to instantiate a parallel X! Tandem module is worth the cost for PR. In addition, from timings gathered during experiments, a general formula was derived for determining the value of performing PR upon a fast module

Modeling and Analysis of Space Based Transceivers

This paper presents the tool chain, methodology, and initial results of a study to provide a thorough, objective, and quantitative analysis of the design alternatives for space Software Defined Radio (SDR) transceivers. The approach taken was to develop a set of models and tools for describing communications requirements, the algorithm resource requirements, the available hardware, and the alternative software architectures, and generate analysis data necessary to compare alternative designs. The Space Transceiver Analysis Tool (STAT) was developed to help users identify and select representative designs, calculate the analysis data, and perform a comparative analysis of the representative designs. The tool allows the design space to be searched quickly while permitting incremental refinement in regions of higher payoff

Kodizajn arhitekture i algoritama za lokalizacijumobilnih robota i detekciju prepreka baziranih namodelu

This thesis proposes SoPC (System on a Programmable Chip) architectures for efficient embedding of vison-based localization and obstacle detection tasks in a navigational pipeline on autonomous mobile robots. The obtained results are equivalent or better in comparison to state-ofthe- art. For localization, an efficient hardware architecture that supports EKF-SLAM's local map management with seven-dimensional landmarks in real time is developed. For obstacle detection a novel method of object recognition is proposed - detection by identification framework based on single detection window scale. This framework allows adequate algorithmic precision and execution speeds on embedded hardware platforms.Ova teza bavi se dizajnom SoPC (engl. System on a Programmable Chip) arhitektura i algoritama za efikasnu implementaciju zadataka lokalizacije i detekcije prepreka baziranih na viziji u kontekstu autonomne robotske navigacije. Za lokalizaciju, razvijena je efikasna računarska arhitektura za EKF-SLAM algoritam, koja podržava skladištenje i obradu sedmodimenzionalnih orijentira lokalne mape u realnom vremenu. Za detekciju prepreka je predložena nova metoda prepoznavanja objekata u slici putem prozora detekcije fiksne dimenzije, koja omogućava veću brzinu izvršavanja algoritma detekcije na namenskim računarskim platformama

Run-time management for future MPSoC platforms

In recent years, we are witnessing the dawning of the Multi-Processor Systemon- Chip (MPSoC) era. In essence, this era is triggered by the need to handle more complex applications, while reducing overall cost of embedded (handheld) devices. This cost will mainly be determined by the cost of the hardware platform and the cost of designing applications for that platform. The cost of a hardware platform will partly depend on its production volume. In turn, this means that ??exible, (easily) programmable multi-purpose platforms will exhibit a lower cost. A multi-purpose platform not only requires ??exibility, but should also combine a high performance with a low power consumption. To this end, MPSoC devices integrate computer architectural properties of various computing domains. Just like large-scale parallel and distributed systems, they contain multiple heterogeneous processing elements interconnected by a scalable, network-like structure. This helps in achieving scalable high performance. As in most mobile or portable embedded systems, there is a need for low-power operation and real-time behavior. The cost of designing applications is equally important. Indeed, the actual value of future MPSoC devices is not contained within the embedded multiprocessor IC, but in their capability to provide the user of the device with an amount of services or experiences. So from an application viewpoint, MPSoCs are designed to ef??ciently process multimedia content in applications like video players, video conferencing, 3D gaming, augmented reality, etc. Such applications typically require a lot of processing power and a signi??cant amount of memory. To keep up with ever evolving user needs and with new application standards appearing at a fast pace, MPSoC platforms need to be be easily programmable. Application scalability, i.e. the ability to use just enough platform resources according to the user requirements and with respect to the device capabilities is also an important factor. Hence scalability, ??exibility, real-time behavior, a high performance, a low power consumption and, ??nally, programmability are key components in realizing the success of MPSoC platforms. The run-time manager is logically located between the application layer en the platform layer. It has a crucial role in realizing these MPSoC requirements. As it abstracts the platform hardware, it improves platform programmability. By deciding on resource assignment at run-time and based on the performance requirements of the user, the needs of the application and the capabilities of the platform, it contributes to ??exibility, scalability and to low power operation. As it has an arbiter function between different applications, it enables real-time behavior. This thesis details the key components of such an MPSoC run-time manager and provides a proof-of-concept implementation. These key components include application quality management algorithms linked to MPSoC resource management mechanisms and policies, adapted to the provided MPSoC platform services. First, we describe the role, the responsibilities and the boundary conditions of an MPSoC run-time manager in a generic way. This includes a de??nition of the multiprocessor run-time management design space, a description of the run-time manager design trade-offs and a brief discussion on how these trade-offs affect the key MPSoC requirements. This design space de??nition and the trade-offs are illustrated based on ongoing research and on existing commercial and academic multiprocessor run-time management solutions. Consequently, we introduce a fast and ef??cient resource allocation heuristic that considers FPGA fabric properties such as fragmentation. In addition, this thesis introduces a novel task assignment algorithm for handling soft IP cores denoted as hierarchical con??guration. Hierarchical con??guration managed by the run-time manager enables easier application design and increases the run-time spatial mapping freedom. In turn, this improves the performance of the resource assignment algorithm. Furthermore, we introduce run-time task migration components. We detail a new run-time task migration policy closely coupled to the run-time resource assignment algorithm. In addition to detailing a design-environment supported mechanism that enables moving tasks between an ISP and ??ne-grained recon??gurable hardware, we also propose two novel task migration mechanisms tailored to the Network-on-Chip environment. Finally, we propose a novel mechanism for task migration initiation, based on reusing debug registers in modern embedded microprocessors. We propose a reactive on-chip communication management mechanism. We show that by exploiting an injection rate control mechanism it is possible to provide a communication management system capable of providing a soft (reactive) QoS in a NoC. We introduce a novel, platform independent run-time algorithm to perform quality management, i.e. to select an application quality operating point at run-time based on the user requirements and the available platform resources, as reported by the resource manager. This contribution also proposes a novel way to manage the interaction between the quality manager and the resource manager. In order to have a the realistic, reproducible and ??exible run-time manager testbench with respect to applications with multiple quality levels and implementation tradev offs, we have created an input data generation tool denoted Pareto Surfaces For Free (PSFF). The the PSFF tool is, to the best of our knowledge, the ??rst tool that generates multiple realistic application operating points either based on pro??ling information of a real-life application or based on a designer-controlled random generator. Finally, we provide a proof-of-concept demonstrator that combines these concepts and shows how these mechanisms and policies can operate for real-life situations. In addition, we show that the proposed solutions can be integrated into existing platform operating systems

Automated Debugging Methodology for FPGA-based Systems

Electronic devices make up a vital part of our lives. These are seen from mobiles, laptops, computers, home automation, etc. to name a few. The modern designs constitute billions of transistors. However, with this evolution, ensuring that the devices fulfill the designer’s expectation under variable conditions has also become a great challenge. This requires a lot of design time and effort. Whenever an error is encountered, the process is re-started. Hence, it is desired to minimize the number of spins required to achieve an error-free product, as each spin results in loss of time and effort. Software-based simulation systems present the main technique to ensure the verification of the design before fabrication. However, few design errors (bugs) are likely to escape the simulation process. Such bugs subsequently appear during the post-silicon phase. Finding such bugs is time-consuming due to inherent invisibility of the hardware. Instead of software simulation of the design in the pre-silicon phase, post-silicon techniques permit the designers to verify the functionality through the physical implementations of the design. The main benefit of the methodology is that the implemented design in the post-silicon phase runs many order-of-magnitude faster than its counterpart in pre-silicon. This allows the designers to validate their design more exhaustively. This thesis presents five main contributions to enable a fast and automated debugging solution for reconfigurable hardware. During the research work, we used an obstacle avoidance system for robotic vehicles as a use case to illustrate how to apply the proposed debugging solution in practical environments. The first contribution presents a debugging system capable of providing a lossless trace of debugging data which permits a cycle-accurate replay. This methodology ensures capturing permanent as well as intermittent errors in the implemented design. The contribution also describes a solution to enhance hardware observability. It is proposed to utilize processor-configurable concentration networks, employ debug data compression to transmit the data more efficiently, and partially reconfiguring the debugging system at run-time to save the time required for design re-compilation as well as preserve the timing closure. The second contribution presents a solution for communication-centric designs. Furthermore, solutions for designs with multi-clock domains are also discussed. The third contribution presents a priority-based signal selection methodology to identify the signals which can be more helpful during the debugging process. A connectivity generation tool is also presented which can map the identified signals to the debugging system. The fourth contribution presents an automated error detection solution which can help in capturing the permanent as well as intermittent errors without continuous monitoring of debugging data. The proposed solution works for designs even in the absence of golden reference. The fifth contribution proposes to use artificial intelligence for post-silicon debugging. We presented a novel idea of using a recurrent neural network for debugging when a golden reference is present for training the network. Furthermore, the idea was also extended to designs where golden reference is not present

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

PRODUCTIVELY SCALING HARDWARE DESIGNS OVER INCREASING RESOURCES USING A SYSTEMATIC DESIGN ANALYSIS APPROACH

As processor development shifts from strict single core frequency scaling to het- erogeneous resource scaling two important considerations require evaluation. First, how to design systems with an increasing amount of heterogeneous resources, and second, how to maintain a designer’s productivity as the number of possible con- figurations grows. Therefore, it is necessary to determine what useful information can be gathered from existing designs to help predict or identify a design’s potential scalability, as well as, identifying which routine tasks can be automated to improve a designer’s productivity. Moreover, once this information is collected, how can this information be conveyed to the designer such that it can be used to increase overall productivity when implementing the design over increasing amounts of resources? This research looks at various approaches to analyze designs and attempts to distribute an application efficiently across a heterogeneous cluster of computing re- sources through the use of a Systematic Design Analysis flow and an assortment of productivity tools. These tools provide the designer with projections on the amount of resources needed to scale an existing design to a specified performance, as well as, projecting the performance based on a specified amount of resources. This is accomplished through the combination of static HDL profiling, component synthesis resource utilization, and runtime performance monitoring. For evaluation, four case studies are presented to demonstrate the proposed flow’s scalability on a small scale cluster of FPGAs. The results are highly favorable, providing orders of magnitude speedup with minimal intervention from the designer