Design and Programming Methods for Reconfigurable Multi-Core Architectures using a Network-on-Chip-Centric Approach

A current trend in the semiconductor industry is the use of Multi-Processor Systems-on-Chip (MPSoCs) for a wide variety of applications such as image processing, automotive, multimedia, and robotic systems. Most applications gain performance advantages by executing parallel tasks on multiple processors due to the inherent parallelism. Moreover, heterogeneous structures provide high performance/energy efficiency, since application-specific processing elements (PEs) can be exploited. The increasing number of heterogeneous PEs leads to challenging communication requirements. To overcome this challenge, Networks-on-Chip (NoCs) have emerged as scalable on-chip interconnect. Nevertheless, NoCs have to deal with many design parameters such as virtual channels, routing algorithms and buffering techniques to fulfill the system requirements. This thesis highly contributes to the state-of-the-art of FPGA-based MPSoCs and NoCs. In the following, the three major contributions are introduced. As a first major contribution, a novel router concept is presented that efficiently utilizes communication times by performing sequences of arithmetic operations on the data that is transferred. The internal input buffers of the routers are exchanged with processing units that are capable of executing operations. Two different architectures of such processing units are presented. The first architecture provides multiply and accumulate operations which are often used in signal processing applications. The second architecture introduced as Application-Specific Instruction Set Routers (ASIRs) contains a processing unit capable of executing any operation and hence, it is not limited to multiply and accumulate operations. An internal processing core located in ASIRs can be developed in C/C++ using high-level synthesis. The second major contribution comprises application and performance explorations of the novel router concept. Models that approximate the achievable speedup and the end-to-end latency of ASIRs are derived and discussed to show the benefits in terms of performance. Furthermore, two applications using an ASIR-based MPSoC are implemented and evaluated on a Xilinx Zynq SoC. The first application is an image processing algorithm consisting of a Sobel filter, an RGB-to-Grayscale conversion, and a threshold operation. The second application is a system that helps visually impaired people by navigating them through unknown indoor environments. A Light Detection and Ranging (LIDAR) sensor scans the environment, while Inertial Measurement Units (IMUs) measure the orientation of the user to generate an audio signal that makes the distance as well as the orientation of obstacles audible. This application consists of multiple parallel tasks that are mapped to an ASIR-based MPSoC. Both applications show the performance advantages of ASIRs compared to a conventional NoC-based MPSoC. Furthermore, dynamic partial reconfiguration in terms of relocation and security aspects are investigated. The third major contribution refers to development and programming methodologies of NoC-based MPSoCs. A software-defined approach is presented that combines the design and programming of heterogeneous MPSoCs. In addition, a Kahn-Process-Network (KPN) –based model is designed to describe parallel applications for MPSoCs using ASIRs. The KPN-based model is extended to support not only the mapping of tasks to NoC-based MPSoCs but also the mapping to ASIR-based MPSoCs. A static mapping methodology is presented that assigns tasks to ASIRs and processors for a given KPN-model. The impact of external hardware components such as sensors, actuators and accelerators connected to the processors is also discussed which makes the approach of high interest for embedded systems

Achieving a better balance between productivity and performance on FPGAs through Heterogeneous Extensible Multiprocessor Systems

Field Programmable Gate Arrays (FPGAs) were first introduced circa 1980, and they held the promise of delivering performance levels associated with customized circuits, but with productivity levels more closely associated with software development. Achieving both performance and productivity objectives has been a long standing challenge problem for the reconfigurable computing community and remains unsolved today. On one hand, Vendor supplied design flows have tended towards achieving the high levels of performance through gate level customization, but at the cost of very low productivity. On the other hand, FPGA densities are following Moore\u27s law and and can now support complete multiprocessor system architectures. Thus FPGAs can be turned into an architecture with programmable processors which brings productivity but sacrifices the peak performance advantages of custom circuits. In this thesis we explore how the two use cases can be combined to achieve the best from both. The flexibility of the FPGAs to host a heterogeneous multiprocessor system with different types of programmable processors and custom accelerators allows the software developers to design a platform that matches the unique performance needs of their application. However, currently no automated approaches are publicly available to create such heterogeneous architectures as well as the software support for these platforms. Creating base architectures, configuring multiple tool chains, and repetitive engineering design efforts can and should be automated. This thesis introduces Heterogeneous Extensible Multiprocessor System (HEMPS) template approach which allows an FPGA to be programmed with productivity levels close to those associated with parallel processing, and with performance levels close to those associated with customized circuits. The work in this thesis introduces an ArchGen script to automate the generation of HEMPS systems as well as a library of portable and self tuning polymorphic functions. These tools will abstract away the HW/SW co-design details and provide a transparent programming language to capture different levels of parallelisms, without sacrificing productivity or portability

Exploring Processor and Memory Architectures for Multimedia

Multimedia has become one of the cornerstones of our 21st century society and, when combined with mobility, has enabled a tremendous evolution of our society. However, joining these two concepts introduces many technical challenges. These range from having sufficient performance for handling multimedia content to having the battery stamina for acceptable mobile usage. When taking a projection of where we are heading, we see these issues becoming ever more challenging by increased mobility as well as advancements in multimedia content, such as introduction of stereoscopic 3D and augmented reality. The increased performance needs for handling multimedia come not only from an ongoing step-up in resolution going from QVGA (320x240) to Full HD (1920x1080) a 27x increase in less than half a decade. On top of this, there is also codec evolution (MPEG-2 to H.264 AVC) that adds to the computational load increase. To meet these performance challenges there has been processing and memory architecture advances (SIMD, out-of-order superscalarity, multicore processing and heterogeneous multilevel memories) in the mobile domain, in conjunction with ever increasing operating frequencies (200MHz to 2GHz) and on-chip memory sizes (128KB to 2-3MB). At the same time there is an increase in requirements for mobility, placing higher demands on battery-powered systems despite the steady increase in battery capacity (500 to 2000mAh). This leaves negative net result in-terms of battery capacity versus performance advances. In order to make optimal use of these architectural advances and to meet the power limitations in mobile systems, there is a need for taking an overall approach on how to best utilize these systems. The right trade-off between performance and power is crucial. On top of these constraints, the flexibility aspects of the system need to be addressed. All this makes it very important to reach the right architectural balance in the system. The first goal for this thesis is to examine multimedia applications and propose a flexible solution that can meet the architectural requirements in a mobile system. Secondly, propose an automated methodology of optimally mapping multimedia data and instructions to a heterogeneous multilevel memory subsystem. The proposed methodology uses constraint programming for solving a multidimensional optimization problem. Results from this work indicate that using today’s most advanced mobile processor technology together with a multi-level heterogeneous on-chip memory subsystem can meet the performance requirements for handling multimedia. By utilizing the automated optimal memory mapping method presented in this thesis lower total power consumption can be achieved, whilst performance for multimedia applications is improved, by employing enhanced memory management. This is achieved through reduced external accesses and better reuse of memory objects. This automatic method shows high accuracy, up to 90%, for predicting multimedia memory accesses for a given architecture

A RISC-V-based FPGA Overlay to Simplify Embedded Accelerator Deployment

Modern cyber-physical systems (CPS) are increasingly adopting heterogeneous systems-on-chip (HeSoCs) as a computing platform to satisfy the demands of their sophisticated workloads. FPGA-based HeSoCs can reach high performance and energy efficiency at the cost of increased design complexity. High-Level Synthesis (HLS) can ease IP design, but automated tools still lack the maturity to efficiently and easily tackle system-level integration of the many hardware and software blocks included in a modern CPS. We present an innovative hardware overlay offering plug-and-play integration of HLS-compiled or handcrafted acceleration IPs thanks to a customizable wrapper attached to the overlay interconnect and providing shared-memory communication to the overlay cores. The latter are based on the open RISC-V ISA and offer simplified software management of the acceleration IP. Deploying the proposed overlay on a Xilinx ZU9EG shows ≈ 20% LUT usage and ≈ 4× speedup compared to program execution on the ARM host core

FPGA structures for high speed and low overhead dynamic circuit specialization

A Field Programmable Gate Array (FPGA) is a programmable digital electronic chip. The FPGA does not come with a predefined function from the manufacturer; instead, the developer has to define its function through implementing a digital circuit on the FPGA resources. The functionality of the FPGA can be reprogrammed as desired and hence the name “field programmable”. FPGAs are useful in small volume digital electronic products as the design of a digital custom chip is expensive. Changing the FPGA (also called configuring it) is done by changing the configuration data (in the form of bitstreams) that defines the FPGA functionality. These bitstreams are stored in a memory of the FPGA called configuration memory. The SRAM cells of LookUp Tables (LUTs), Block Random Access Memories (BRAMs) and DSP blocks together form the configuration memory of an FPGA. The configuration data can be modified according to the user’s needs to implement the user-defined hardware. The simplest way to program the configuration memory is to download the bitstreams using a JTAG interface. However, modern techniques such as Partial Reconfiguration (PR) enable us to configure a part in the configuration memory with partial bitstreams during run-time. The reconfiguration is achieved by swapping in partial bitstreams into the configuration memory via a configuration interface called Internal Configuration Access Port (ICAP). The ICAP is a hardware primitive (macro) present in the FPGA used to access the configuration memory internally by an embedded processor. The reconfiguration technique adds flexibility to use specialized ci rcuits that are more compact and more efficient t han t heir b ulky c ounterparts. An example of such an implementation is the use of specialized multipliers instead of big generic multipliers in an FIR implementation with constant coefficients. To specialize these circuits and reconfigure during the run-time, researchers at the HES group proposed the novel technique called parameterized reconfiguration that can be used to efficiently and automatically implement Dynamic Circuit Specialization (DCS) that is built on top of the Partial Reconfiguration method. It uses the run-time reconfiguration technique that is tailored to implement a parameterized design. An application is said to be parameterized if some of its input values change much less frequently than the rest. These inputs are called parameters. Instead of implementing these parameters as regular inputs, in DCS these inputs are implemented as constants, and the application is optimized for the constants. For every change in parameter values, the design is re-optimized (specialized) during run-time and implemented by reconfiguring the optimized design for a new set of parameters. In DCS, the bitstreams of the parameterized design are expressed as Boolean functions of the parameters. For every infrequent change in parameters, a specialized FPGA configuration is generated by evaluating the corresponding Boolean functions, and the FPGA is reconfigured with the specialized configuration. A detailed study of overheads of DCS and providing suitable solutions with appropriate custom FPGA structures is the primary goal of the dissertation. I also suggest different improvements to the FPGA configuration memory architecture. After offering the custom FPGA structures, I investigated the role of DCS on FPGA overlays and the use of custom FPGA structures that help to reduce the overheads of DCS on FPGA overlays. By doing so, I hope I can convince the developer to use DCS (which now comes with minimal costs) in real-world applications. I start the investigations of overheads of DCS by implementing an adaptive FIR filter (using the DCS technique) on three different Xilinx FPGA platforms: Virtex-II Pro, Virtex-5, and Zynq-SoC. The study of how DCS behaves and what is its overhead in the evolution of the three FPGA platforms is the non-trivial basis to discover the costs of DCS. After that, I propose custom FPGA structures (reconfiguration controllers and reconfiguration drivers) to reduce the main overhead (reconfiguration time) of DCS. These structures not only reduce the reconfiguration time but also help curbing the power hungry part of the DCS system. After these chapters, I study the role of DCS on FPGA overlays. I investigate the effect of the proposed FPGA structures on Virtual-Coarse-Grained Reconfigurable Arrays (VCGRAs). I classify the VCGRA implementations into three types: the conventional VCGRA, partially parameterized VCGRA and fully parameterized VCGRA depending upon the level of parameterization. I have designed two variants of VCGRA grids for HPC image processing applications, namely, the MAC grid and Pixie. Finally, I try to tackle the reconfiguration time overhead at the hardware level of the FPGA by customizing the FPGA configuration memory architecture. In this part of my research, I propose to use a parallel memory structure to improve the reconfiguration time of DCS drastically. However, this improvement comes with a significant overhead of hardware resources which will need to be solved in future research on commercial FPGA configuration memory architectures

Memory hierarchy and data communication in heterogeneous reconfigurable SoCs

The miniaturization race in the hardware industry aiming at continuous increasing of transistor density on a die does not bring respective application performance improvements any more. One of the most promising alternatives is to exploit a heterogeneous nature of common applications in hardware. Supported by reconfigurable computation, which has already proved its efficiency in accelerating data intensive applications, this concept promises a breakthrough in contemporary technology development. Memory organization in such heterogeneous reconfigurable architectures becomes very critical. Two primary aspects introduce a sophisticated trade-off. On the one hand, a memory subsystem should provide well organized distributed data structure and guarantee the required data bandwidth. On the other hand, it should hide the heterogeneous hardware structure from the end-user, in order to support feasible high-level programmability of the system. This thesis work explores the heterogeneous reconfigurable hardware architectures and presents possible solutions to cope the problem of memory organization and data structure. By the example of the MORPHEUS heterogeneous platform, the discussion follows the complete design cycle, starting from decision making and justification, until hardware realization. Particular emphasis is made on the methods to support high system performance, meet application requirements, and provide a user-friendly programmer interface. As a result, the research introduces a complete heterogeneous platform enhanced with a hierarchical memory organization, which copes with its task by means of separating computation from communication, providing reconfigurable engines with computation and configuration data, and unification of heterogeneous computational devices using local storage buffers. It is distinguished from the related solutions by distributed data-flow organization, specifically engineered mechanisms to operate with data on local domains, particular communication infrastructure based on Network-on-Chip, and thorough methods to prevent computation and communication stalls. In addition, a novel advanced technique to accelerate memory access was developed and implemented

A Model-based Design Framework for Application-specific Heterogeneous Systems

The increasing heterogeneity of computing systems enables higher performance and power efficiency. However, these improvements come at the cost of increasing the overall complexity of designing such systems. These complexities include constructing implementations for various types of processors, setting up and configuring communication protocols, and efficiently scheduling the computational work. The process for developing such systems is iterative and time consuming, with no well-defined performance goal. Current performance estimation approaches use source code implementations that require experienced developers and time to produce. We present a framework to aid in the design of heterogeneous systems and the performance tuning of applications. Our framework supports system construction: integrating custom hardware accelerators with existing cores into processors, integrating processors into cohesive systems, and mapping computations to processors to achieve overall application performance and efficient hardware usage. It also facilitates effective design space exploration using processor models (for both existing and future processors) that do not require source code implementations to estimate performance. We evaluate our framework using a variety of applications and implement them in systems ranging from low power embedded systems-on-chip (SoC) to high performance systems consisting of commercial-off-the-shelf (COTS) components. We show how the design process is improved, reducing the number of design iterations and unnecessary source code development ultimately leading to higher performing efficient systems