31 research outputs found
Software-Defined Radio FPGA Cores: Building towards a Domain-Specific Language
This paper reports on the design and implementation of an open-source library of parameterizable and reusable Hardware Description Language (HDL) Intellectual Property (IP) cores designed for the development of Software-Defined Radio (SDR) applications that are deployed on FPGA-based reconfigurable computing platforms. The library comprises a set of cores that were chosen, together with their parameters and interfacing schemas, based on recommendations from industry and academic SDR experts. The operation of the SDR cores is first validated and then benchmarked against two other cores libraries of a similar type to show that our cores do not take much more logic elements than existing cores and that they support a comparable maximum clock speed. Finally, we propose our design for a Domain-Specific Language (DSL) and supporting tool-flow, which we are in the process of building using our SDR library and the Delite DSL framework. We intend to take this DSL and supporting framework further to provide a rapid prototyping system for SDR application development to programmers not experienced in HDL coding. We conclude with a summary of the main characteristics of our SDR library and reflect on how our DSL tool-flow could assist other developers working in SDR field
High-Level Design for Ultra-Fast Software Defined Radio Prototyping on Multi-Processors Heterogeneous Platforms
International audienceThe design of Software Defined Radio (SDR) equipments (terminals, base stations, etc.) is still very challenging. We propose here a design methodology for ultra-fast prototyping on heterogeneous platforms made of GPPs (General Purpose Processors), DSPs (Digital Signal Processors) and FPGAs (Field Programmable Gate Array). Lying on a component-based approach, the methodology mainly aims at automating as much as possible the design from an algorithmic validation to a multi-processing heterogeneous implementation. The proposed methodology is based on the SynDEx CAD design approach, which was originally dedicated to multi-GPPs networks. We show how this was changed so that it is made appropriate with an embedded context of DSP. The implication of FPGAs is then addressed and integrated in the design approach with very little restrictions. Apart from a manual HW/SW partitioning, all other operations may be kept automatic in a heterogeneous processing context. The targeted granularity of the components, which are to be assembled in the design flow, is roughly the same size as that of a FFT, a filter or a Viterbi decoder for instance. The re-use of third party or pre-developed IPs is a basis for this design approach. Thanks to the proposed design methodology it is possible to port "ultra" fast a radio application over several platforms. In addition, the proposed design methodology is not restricted to SDR equipment design, and can be useful for any real-time embedded heterogeneous design in a prototyping context
SdrLift: A Domain-Specific Intermediate Hardware Synthesis Framework for Prototyping Software-Defined Radios
Modern design of Software-Defined Radio (SDR) applications is based on Field Programmable Gate Arrays (FPGA) due to their ability to be configured into solution architectures that are well suited to domain-specific problems while achieving the best trade-off between performance, power, area, and flexibility. FPGAs are well known for rich computational resources, which traditionally include logic, register, and routing resources. The increased technological advances have seen FPGAs incorporating more complex components that comprise sophisticated memory blocks, Digital Signal Processing (DSP) blocks, and high-speed interfacing to Gigabit Ethernet (GbE) and Peripheral Component Interconnect Express (PCIe) bus. Gateware for programming FPGAs is described at a lowlevel of design abstraction using Register Transfer Language (RTL), typically using either VHSIC-HDL (VHDL) or Verilog code. In practice, the low-level description languages have a very steep learning curve, provide low productivity for hardware designers and lack readily available open-source library support for fundamental designs, and consequently limit the design to only hardware experts. These limitations have led to the adoption of High-Level Synthesis (HLS) tools that raise design abstraction using syntax, semantics, and software development notations that are well-known to most software developers. However, while HLS has made programming of FPGAs more accessible and can increase the productivity of design, they are still not widely adopted in the design community due to the low-level skills that are still required to produce efficient designs. Additionally, the resultant RTL code from HLS tools is often difficult to decipher, modify and optimize due to the functionality and micro-architecture that are coupled together in a single High-Level Language (HLL). In order to alleviate these problems, Domain-Specific Languages (DSL) have been introduced to capture algorithms at a high level of abstraction with more expressive power and providing domain-specific optimizations that factor in new transformations and the trade-off between resource utilization and system performance. The problem of existing DSLs is that they are designed around imperative languages with an instruction sequence that does not match the hardware structure and intrinsics, leading to hardware designs with system properties that are unconformable to the high-level specifications and constraints. The aim of this thesis is, therefore, to design and implement an intermediatelevel framework namely SdrLift for use in high-level rapid prototyping of SDR applications that are based on an FPGA. The SdrLift input is a HLL developed using functional language constructs and design patterns that specify the structural behavior of the application design. The functionality of the SdrLift language is two-fold, first, it can be used directly by a designer to develop the SDR applications, secondly, it can be used as the Intermediate Representation (IR) step that is generated by a higher-level language or a DSL. The SdrLift compiler uses the dataflow graph as an IR to structurally represent the accelerator micro-architecture in which the components correspond to the fine-level and coarse-level Hardware blocks (HW Block) which are either auto-synthesized or integrated from existing reusable Intellectual Property (IP) core libraries. Another IR is in the form of a dataflow model and it is used for composition and global interconnection of the HW Blocks while making efficient interfacing decisions in an attempt to satisfy speed and resource usage objectives. Moreover, the dataflow model provides rules and properties that will be used to provide a theoretical framework that formally analyzes the characteristics of SDR applications (i.e. the throughput, sample rate, latency, and buffer size among other factors). Using both the directed graph flow (DFG) and the dataflow model in the SdrLift compiler provides two benefits: an abstraction of the microarchitecture from the high-level algorithm specifications and also decoupling of the microarchitecture from the low-level RTL implementation. Following the IR creation and model analyses is the VHDL code generation which employs the low-level optimizations that ensure optimal hardware design results. The code generation process per forms analysis to ensure the resultant hardware system conforms to the high-level design specifications and constraints. SdrLift is evaluated by developing representative SDR case studies, in which the VHDL code for eight different SDR applications is generated. The experimental results show that SdrLift achieves the desired performance and flexibility, while also conserving the hardware resources utilized
FPGA-based implementation of real-time identification procedures for adaptive control in photovoltaic applications
2013 - 2014In this thesis two adaptive Maximum Power Point Tracking (MPPT) techniques for PhotoVoltaic (PV) applications, which are based on two different real-time identification procedures are proposed. The algorithms are implemented on the same low-cost Field Programmable Gate Array (FPGA) device in charge of controlling the switching converter that processes the power produced by the PV array.
The Perturb & Observe (P&O) algorithm is the most common MPPT technique. Its efficiency is mainly related to two parameters: the perturbation amplitude and the perturbation period Tp. The optimal values of such parameters depend on the PV array type and on the irradiance and temperature conditions thereof, as well as on the parameters of the power processing circuit. Thus, a method for dynamically adapt the P&O parameters would be very useful for increasing the P&O MPPT performances. Several approaches presented in the current literature are focused on the adaptation of the perturbation amplitude. In this thesis, on the contrary, the on-line optimization of the value of Tp is proposed. The effects of such a parameter on both the tracking speed and the stationary MPPT efficiency are pointed out. Besides, the need for a real-time identification technique for identifying the minimum acceptable value of Tp in the actual PV operating conditions is demonstrated.
Two different identification procedures aimed at developing the aforementioned adaptive MPPT controllers have been studied: the Cross-Correlation Method (CCM) and the Dual Kalman Filter (DKF). The first one belongs to the non-parametric techniques and allows identifying the impulse response and the frequency response of the PV system. Instead, the DKF is a model-based approach which estimates the states and the parameters of the system. One of the aims of this thesis is to demonstrate the usefulness of these identification procedures for the optimization of the PV P&O MPPT performances.
In order to achieve a good trade-off between the desired performances and the cost of the controller, hardware digital solutions, such as FPGA, are adopted. They are able to reduce the execution time by exploiting the intrinsic parallelism of the algorithm to be implemented. Then, in this work, the challenging design of a high performances hardware architecture for the identification algorithms is dealt with. Moreover, the implemented identification techniques are compared in terms of accuracy, identification time and used hardware resources.
Several simulations and experimental tests demonstrate the feasibility of the developed identification procedures. In fact, the proposed adaptive MPPT controllers suitably change in few tens of milliseconds the value of Tp ensuring a stable MPPT behaviour. The developed FPGA-based architectures of both the identification techniques is promising for embedding other functions that are of interest in the field of PV systems, e.g. related to on-line monitoring or diagnostic purposes.
The work has been developed in co-tutorship between the Systèmes et Applications des Technologies de l’Information et de l’Energie (SATIE) laboratory in the Université de Cergy-Pontoise (France) and the Circuiti Elettronici di Potenza laboratory in the Universitá degli Studi di Salerno (Italy). The work has been supported by the Université Franco-Italienne by means the Vinci project 2013 n. C2-29. [edited by author]XIII n.s
Simulation Native des Systèmes Multiprocesseurs sur Puce à l'aide de la Virtualisation Assistée par le Matériel
L'intégration de plusieurs processeurs hétérogènes en un seul système sur puce (SoC) est une tendance claire dans les systèmes embarqués. La conception et la vérification de ces systèmes nécessitent des plateformes rapides de simulation, et faciles à construire. Parmi les approches de simulation de logiciels, la simulation native est un bon candidat grâce à l'exécution native de logiciel embarqué sur la machine hôte, ce qui permet des simulations à haute vitesse, sans nécessiter le développement de simulateurs d'instructions. Toutefois, les techniques de simulation natives existantes exécutent le logiciel de simulation dans l'espace de mémoire partagée entre le matériel modélisé et le système d'exploitation hôte. Il en résulte de nombreux problèmes, par exemple les conflits l'espace d'adressage et les chevauchements de mémoire ainsi que l'utilisation des adresses de la machine hôte plutôt des celles des plates-formes matérielles cibles. Cela rend pratiquement impossible la simulation native du code existant fonctionnant sur la plate-forme cible. Pour surmonter ces problèmes, nous proposons l'ajout d'une couche transparente de traduction de l'espace adressage pour séparer l'espace d'adresse cible de celui du simulateur de hôte. Nous exploitons la technologie de virtualisation assistée par matériel (HAV pour Hardware-Assisted Virtualization) à cet effet. Cette technologie est maintenant disponibles sur plupart de processeurs grande public à usage général. Les expériences montrent que cette solution ne dégrade pas la vitesse de simulation native, tout en gardant la possibilité de réaliser l'évaluation des performances du logiciel simulé. La solution proposée est évolutive et flexible et nous fournit les preuves nécessaires pour appuyer nos revendications avec des solutions de simulation multiprocesseurs et hybrides. Nous abordons également la simulation d'exécutables cross- compilés pour les processeurs VLIW (Very Long Instruction Word) en utilisant une technique de traduction binaire statique (SBT) pour généré le code natif. Ainsi il n'est pas nécessaire de faire de traduction à la volée ou d'interprétation des instructions. Cette approche est intéressante dans les situations où le code source n'est pas disponible ou que la plate-forme cible n'est pas supporté par les compilateurs reciblable, ce qui est généralement le cas pour les processeurs VLIW. Les simulateurs générés s'exécutent au-dessus de notre plate-forme basée sur le HAV et modélisent les processeurs de la série C6x de Texas Instruments (TI). Les résultats de simulation des binaires pour VLIW montrent une accélération de deux ordres de grandeur par rapport aux simulateurs précis au cycle près.Integration of multiple heterogeneous processors into a single System-on-Chip (SoC) is a clear trend in embedded systems. Designing and verifying these systems require high-speed and easy-to-build simulation platforms. Among the software simulation approaches, native simulation is a good candidate since the embedded software is executed natively on the host machine, resulting in high speed simulations and without requiring instruction set simulator development effort. However, existing native simulation techniques execute the simulated software in memory space shared between the modeled hardware and the host operating system. This results in many problems, including address space conflicts and overlaps as well as the use of host machine addresses instead of the target hardware platform ones. This makes it practically impossible to natively simulate legacy code running on the target platform. To overcome these issues, we propose the addition of a transparent address space translation layer to separate the target address space from that of the host simulator. We exploit the Hardware-Assisted Virtualization (HAV) technology for this purpose, which is now readily available on almost all general purpose processors. Experiments show that this solution does not degrade the native simulation speed, while keeping the ability to accomplish software performance evaluation. The proposed solution is scalable as well as flexible and we provide necessary evidence to support our claims with multiprocessor and hybrid simulation solutions. We also address the simulation of cross-compiled Very Long Instruction Word (VLIW) executables, using a Static Binary Translation (SBT) technique to generated native code that does not require run-time translation or interpretation support. This approach is interesting in situations where either the source code is not available or the target platform is not supported by any retargetable compilation framework, which is usually the case for VLIW processors. The generated simulators execute on top of our HAV based platform and model the Texas Instruments (TI) C6x series processors. Simulation results for VLIW binaries show a speed-up of around two orders of magnitude compared to the cycle accurate simulators.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF
Energy-Efficient Recurrent Neural Network Accelerators for Real-Time Inference
Over the past decade, Deep Learning (DL) and Deep Neural Network (DNN) have gone through a rapid development. They are now vastly applied to various applications and have profoundly changed the life of hu- man beings. As an essential element of DNN, Recurrent Neural Networks (RNN) are helpful in processing time-sequential data and are widely used in applications such as speech recognition and machine translation. RNNs are difficult to compute because of their massive arithmetic operations and large memory footprint. RNN inference workloads used to be executed on conventional general-purpose processors including Central Processing Units (CPU) and Graphics Processing Units (GPU); however, they have un- necessary hardware blocks for RNN computation such as branch predictor, caching system, making them not optimal for RNN processing. To accelerate RNN computations and outperform the performance of conventional processors, previous work focused on optimization methods on both software and hardware. On the software side, previous works mainly used model compression to reduce the memory footprint and the arithmetic operations of RNNs. On the hardware side, previous works also designed domain-specific hardware accelerators based on Field Pro- grammable Gate Arrays (FPGA) or Application Specific Integrated Circuits (ASIC) with customized hardware pipelines optimized for efficient pro- cessing of RNNs. By following this software-hardware co-design strategy, previous works achieved at least 10X speedup over conventional processors. Many previous works focused on achieving high throughput with a large batch of input streams. However, in real-time applications, such as gaming Artificial Intellegence (AI), dynamical system control, low latency is more critical. Moreover, there is a trend of offloading neural network workloads to edge devices to provide a better user experience and privacy protection. Edge devices, such as mobile phones and wearable devices, are usually resource-constrained with a tight power budget. They require RNN hard- ware that is more energy-efficient to realize both low-latency inference and long battery life. Brain neurons have sparsity in both the spatial domain and time domain. Inspired by this human nature, previous work mainly explored model compression to induce spatial sparsity in RNNs. The delta network algorithm alternatively induces temporal sparsity in RNNs and can save over 10X arithmetic operations in RNNs proven by previous works.
In this work, we have proposed customized hardware accelerators to exploit temporal sparsity in Gated Recurrent Unit (GRU)-RNNs and Long Short-Term Memory (LSTM)-RNNs to achieve energy-efficient real-time RNN inference. First, we have proposed DeltaRNN, the first-ever RNN accelerator to exploit temporal sparsity in GRU-RNNs. DeltaRNN has achieved 1.2 TOp/s effective throughput with a batch size of 1, which is 15X higher than its related works. Second, we have designed EdgeDRNN to accelerate GRU-RNN edge inference. Compared to DeltaRNN, EdgeDRNN does not rely on on-chip memory to store RNN weights and focuses on reducing off-chip Dynamic Random Access Memory (DRAM) data traffic using a more scalable architecture. EdgeDRNN have realized real-time inference of large GRU-RNNs with submillisecond latency and only 2.3 W wall plug power consumption, achieving 4X higher energy efficiency than commercial edge AI platforms like NVIDIA Jetson Nano. Third, we have used DeltaRNN to realize the first-ever continuous speech recognition sys- tem with the Dynamic Audio Sensor (DAS) as the front-end. The DAS is a neuromorphic event-driven sensor that produces a stream of asyn- chronous events instead of audio data sampled at a fixed sample rate. We have also showcased how an RNN accelerator can be integrated with an event-driven sensor on the same chip to realize ultra-low-power Keyword Spotting (KWS) on the extreme edge. Fourth, we have used EdgeDRNN to control a powered robotic prosthesis using an RNN controller to replace a conventional proportional–derivative (PD) controller. EdgeDRNN has achieved 21 μs latency of running the RNN controller and could maintain stable control of the prosthesis. We have used DeltaRNN and EdgeDRNN to solve these problems to prove their value in solving real-world problems. Finally, we have applied the delta network algorithm on LSTM-RNNs and have combined it with a customized structured pruning method, called Column-Balanced Targeted Dropout (CBTD), to induce spatio-temporal sparsity in LSTM-RNNs. Then, we have proposed another FPGA-based accelerator called Spartus, the first RNN accelerator that exploits spatio- temporal sparsity. Spartus achieved 9.4 TOp/s effective throughput with a batch size of 1, the highest among present FPGA-based RNN accelerators with a power budget around 10 W. Spartus can complete the inference of an LSTM layer having 5 million parameters within 1 μs
Embedded electronic systems driven by run-time reconfigurable hardware
Abstract
This doctoral thesis addresses the design of embedded electronic systems based on run-time reconfigurable hardware technology –available through SRAM-based FPGA/SoC devices– aimed at contributing to enhance the life quality of the human beings. This work does research on the conception of the system architecture and the reconfiguration engine that provides to the FPGA the capability of dynamic partial reconfiguration in order to synthesize, by means of hardware/software co-design, a given application partitioned in processing tasks which are multiplexed in time and space, optimizing thus its physical implementation –silicon area, processing time, complexity, flexibility, functional density, cost and power consumption– in comparison with other alternatives based on static hardware (MCU, DSP, GPU, ASSP, ASIC, etc.). The design flow of such technology is evaluated through the prototyping of several engineering applications (control systems, mathematical coprocessors, complex image processors, etc.), showing a high enough level of maturity for its exploitation in the industry.Resumen
Esta tesis doctoral abarca el diseño de sistemas electrónicos embebidos basados en tecnologÃa hardware dinámicamente reconfigurable –disponible a través de dispositivos lógicos programables SRAM FPGA/SoC– que contribuyan a la mejora de la calidad de vida de la sociedad. Se investiga la arquitectura del sistema y del motor de reconfiguración que proporcione a la FPGA la capacidad de reconfiguración dinámica parcial de sus recursos programables, con objeto de sintetizar, mediante codiseño hardware/software, una determinada aplicación particionada en tareas multiplexadas en tiempo y en espacio, optimizando asà su implementación fÃsica –área de silicio, tiempo de procesado, complejidad, flexibilidad, densidad funcional, coste y potencia disipada– comparada con otras alternativas basadas en hardware estático (MCU, DSP, GPU, ASSP, ASIC, etc.). Se evalúa el flujo de diseño de dicha tecnologÃa a través del prototipado de varias aplicaciones de ingenierÃa (sistemas de control, coprocesadores aritméticos, procesadores de imagen, etc.), evidenciando un nivel de madurez viable ya para su explotación en la industria.Resum
Aquesta tesi doctoral està orientada al disseny de sistemes electrònics empotrats basats en tecnologia hardware dinà micament reconfigurable –disponible mitjançant dispositius lògics programables SRAM FPGA/SoC– que contribueixin a la millora de la qualitat de vida de la societat. S’investiga l’arquitectura del sistema i del motor de reconfiguració que proporcioni a la FPGA la capacitat de reconfiguració dinà mica parcial dels seus recursos programables, amb l’objectiu de sintetitzar, mitjançant codisseny hardware/software, una determinada aplicació particionada en tasques multiplexades en temps i en espai, optimizant aixà la seva implementació fÃsica –à rea de silici, temps de processat, complexitat, flexibilitat, densitat funcional, cost i potència dissipada– comparada amb altres alternatives basades en hardware està tic (MCU, DSP, GPU, ASSP, ASIC, etc.). S’evalúa el fluxe de disseny d’aquesta tecnologia a través del prototipat de varies aplicacions d’enginyeria (sistemes de control, coprocessadors aritmètics, processadors d’imatge, etc.), demostrant un nivell de maduresa viable ja per a la seva explotació a la indústria
Embedded System Design
A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues
Comparative study of tool-flows for rapid prototyping of software-defined radio digital signal processing
This dissertation is a comparative study of tool-flows for rapid prototyping of SDR DSP operations on programmable hardware platforms. The study is divided into two parts, focusing on high-level tool-flows for implementing SDR DSP operations on FPGA and GPU platforms respectively. In this dissertation, the term ‘tool-flow’ refers to a tool or a chain of tools that facilitate the mapping of an application description specified in a programming language into one or more programmable hardware platforms. High-level tool-flows use different techniques, such as high-level synthesis to allow the designer to specify the application from a high level of abstraction and achieve improved productivity without significant degradation in the design’s performance. SDR is an emerging communications technology that is driven by - among other factors – increasing demands for high-speed, interoperable and versatile communications systems. The key idea in SDR is the need to implement as many as possible of the radio functions that were traditionally defined in fixed hardware, in software on programmable hardware processors instead. The most commonly used processors are based on complex parallel computing architectures in order to support the high-speed processing demands of SDR applications, and they include FPGAs, GPUs and multicore general-purpose processors (GPPs) and DSPs. The architectural complexity of these processors results in a corresponding increase in programming methodologies which however impedes their wider adoption in suitable applications domains, including SDR DSP. In an effort to address this, a plethora of different high-level tool-flows have been developed. Several comparative studies of these tool-flows have been done to help – among other benefits – designers in choosing high-level tools to use. However, there are few studies that focus on SDR DSP operations, and most existing comparative studies are not based on well-defined comparison criteria. The approach implemented in this dissertation is to use a system engineering design process, firstly, to define the qualitative comparison criteria in the form of a specification for an ideal high-level SDR DSP tool-flow and, secondly, to implement a FIR filter case study in each of the tool-flows to enable a quantitative comparison in terms of programming effort and performance. The study considers Migen- and MyHDL-based open-source tool-flows for FPGA targets, and CUDA and Open Computing Language (OpenCL) for GPU targets. The ideal high-level SDR DSP tool-flow specification was defined and used to conduct a comparative study of the tools across three main design categories, which included high-level modelling, verification and implementation. For tool-flows targeting GPU platforms, the FIR case study was implemented using each of the tools; it was compiled, executed on a GPU server consisting of 2 GTX Titan-X GPUs and an Intel Core i7 GPP, and lastly profiled. The tools were moreover compared in terms of programming effort, memory transfers cost and overall operation time. With regard to tool-flows with FPGA targets, the FIR case study was developed by using each tool, and then implemented on a Xilinx 7 FPGA and compared in terms of programming effort, logic utilization and timing performance
Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)
ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability