A Catalog and In-Hardware Evaluation of Open-Source Drop-In Compatible RISC-V Softcore Processors
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Configurable RISC-V Softcore Processor for FPGA Implementation
1 Configurable RISC-V softcore processor for FPGA implementation Joao˜ Filipe Monteiro Rodrigues, Instituto Superior Tecnico,´ Universidade de Lisboa Abstract—Over the past years, the processor market has and development of several programming tools. The RISC-V been dominated by proprietary architectures that implement Foundation controls the RISC-V evolution, and its members instruction sets that require licensing and the payment of fees to are responsible for promoting the adoption of RISC-V and receive permission so they can be used. ARM is an example of one of those companies that sell its microarchitectures to participating in the development of the new ISA. In the list of the manufactures so they can implement them into their own members are big companies like Google, NVIDIA, Western products, and it does not allow the use of its instruction set Digital, Samsung, or Qualcomm. (ISA) in other implementations without licensing. The RISC-V The main goal of this work is the development of a RISC- instruction set appeared proposing the hardware and software V softcore processor to be implemented in an FPGA, using development without costs, through the creation of an open- source ISA. This way, it is possible that any project that im- a non-RISC-V core as the base of this architecture. The plements the RISC-V ISA can be made available open-source or proposed solution is focused on solving the problems and even implemented in commercial products. However, the RISC- limitations identified in the other RISC-V cores that were V solutions that have been developed do not present the needed analyzed in this thesis, especially in terms of the adaptability requirements so they can be included in projects, especially the and flexibility, allowing future modifications according to the research projects, because they offer poor documentation, and their performances are not suitable. -
Reconfigurable Computing
Reconfigurable Computing: A Survey of Systems and Software KATHERINE COMPTON Northwestern University AND SCOTT HAUCK University of Washington Due to its potential to greatly accelerate a wide variety of applications, reconfigurable computing has become a subject of a great deal of research. Its key feature is the ability to perform computations in hardware to increase performance, while retaining much of the flexibility of a software solution. In this survey, we explore the hardware aspects of reconfigurable computing machines, from single chip architectures to multi-chip systems, including internal structures and external coupling. We also focus on the software that targets these machines, such as compilation tools that map high-level algorithms directly to the reconfigurable substrate. Finally, we consider the issues involved in run-time reconfigurable systems, which reuse the configurable hardware during program execution. Categories and Subject Descriptors: A.1 [Introductory and Survey]; B.6.1 [Logic Design]: Design Style—logic arrays; B.6.3 [Logic Design]: Design Aids; B.7.1 [Integrated Circuits]: Types and Design Styles—gate arrays General Terms: Design, Performance Additional Key Words and Phrases: Automatic design, field-programmable, FPGA, manual design, reconfigurable architectures, reconfigurable computing, reconfigurable systems 1. INTRODUCTION of algorithms. The first is to use hard- wired technology, either an Application There are two primary methods in con- Specific Integrated Circuit (ASIC) or a ventional computing for the execution group of individual components forming a This research was supported in part by Motorola, Inc., DARPA, and NSF. K. Compton was supported by an NSF fellowship. S. Hauck was supported in part by an NSF CAREER award and a Sloan Research Fellowship. -
Syllabus: EEL4930/5934 Reconfigurable Computing
EEL4720/5721 Reconfigurable Computing (dual-listed course) Department of Electrical and Computer Engineering University of Florida Spring Semester 2019 Catalog Description: Prereq: EEL4712C or EEL5764 or consent of instructor. Fundamental concepts at advanced undergraduate level (EEL4720) and introductory graduate level (EEL5721) in reconfigurable computing (RC) based upon advanced technologies in field-programmable logic devices. Topics include general RC concepts, device architectures, design tools, metrics and kernels, system architectures, and application case studies. Credit Hours: 3 Prerequisites by Topic: Fundamentals of digital design including device technologies, design methodology and techniques, and design environments and tools; fundamentals of computer organization and architecture, including datapath and control structures, data formats, instruction-set principles, pipelining, instruction-level parallelism, memory hierarchy, and interconnects and interfacing. Instructor: Dr. Herman Lam Office: Benton Hall, Room 313 Office hours: TBA Telephone: (352) 392-2689 Email: [email protected] Teaching Assistant: Seyed Hashemi Office hours: TBA Email: [email protected] Class lectures: MWF 4th period, Larsen Hall 239 Required textbook: none References: . Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation, edited by Scott Hauck and Andre DeHon, Elsevier, Inc. (Morgan Kaufmann Publishers), Amsterdam, 2008. ISBN: 978-0-12-370522-8 . C. Maxfield, The Design Warrior's Guide to FPGAs, Newnes, 2004, ISBN: 978-0750676045. -
A MATLAB Compiler for Distributed, Heterogeneous, Reconfigurable
A MATLAB Compiler For Distributed, Heterogeneous, Reconfigurable Computing Systems P. Banerjee, N. Shenoy, A. Choudhary, S. Hauck, C. Bachmann, M. Haldar, P. Joisha, A. Jones, A. Kanhare A. Nayak, S. Periyacheri, M. Walkden, D. Zaretsky Electrical and Computer Engineering Northwestern University 2145 Sheridan Road Evanston, IL-60208 [email protected] Abstract capabilities and are coordinated to perform an application whose subtasks have diverse execution requirements. One Recently, high-level languages such as MATLAB have can visualize such systems to consist of embedded proces- become popular in prototyping algorithms in domains such sors, digital signal processors, specialized chips, and field- as signal and image processing. Many of these applications programmable gate arrays (FPGA) interconnected through whose subtasks have diverse execution requirements, often a high-speed interconnection network; several such systems employ distributed, heterogeneous, reconfigurable systems. have been described in [9]. These systems consist of an interconnected set of heteroge- A key question that needs to be addressed is how to map neous processing resources that provide a variety of archi- a given computation on such a heterogeneous architecture tectural capabilities. The objective of the MATCH (MATlab without expecting the application programmer to get into Compiler for Heterogeneous computing systems) compiler the low level details of the architecture or forcing him/her to project at Northwestern University is to make it easier for understand -
Architecture and Programming Model Support for Reconfigurable Accelerators in Multi-Core Embedded Systems »
THESE DE DOCTORAT DE L’UNIVERSITÉ BRETAGNE SUD COMUE UNIVERSITE BRETAGNE LOIRE ÉCOLE DOCTORALE N° 601 Mathématiques et Sciences et Technologies de l'Information et de la Communication Spécialité : Électronique Par « Satyajit DAS » « Architecture and Programming Model Support For Reconfigurable Accelerators in Multi-Core Embedded Systems » Thèse présentée et soutenue à Lorient, le 4 juin 2018 Unité de recherche : Lab-STICC Thèse N° : 491 Rapporteurs avant soutenance : Composition du Jury : Michael HÜBNER Professeur, Ruhr-Universität François PÊCHEUX Professeur, Sorbonne Université Bochum Président (à préciser après la soutenance) Jari NURMI Professeur, Tampere University of Angeliki KRITIKAKOU Maître de conférences, Université Technology Rennes 1 Davide ROSSI Assistant professor, Université de Bologna Kevin MARTIN Maître de conférences, Université Bretagne Sud Directeur de thèse Philippe COUSSY Professeur, Université Bretagne Sud Co-directeur de thèse Luca BENINI Professeur, Université de Bologna Architecture and Programming Model Support for Reconfigurable Accelerators in Multi-Core Embedded Systems Satyajit Das 2018 iii ABSTRACT Emerging trends in embedded systems and applications need high throughput and low power consumption. Due to the increasing demand for low power computing, and diminishing returns from technology scaling, industry and academia are turning with renewed interest toward energy efficient hardware accelerators. The main drawback of hardware accelerators is that they are not programmable. Therefore, their utilization can be low as they perform one specific function and increasing the number of the accelerators in a system onchip (SoC) causes scalability issues. Programmable accelerators provide flexibility and solve the scalability issues. Coarse-Grained Reconfigurable Array (CGRA) architecture consisting several processing elements with word level granularity is a promising choice for programmable accelerator. -
FPGA Architecture: Survey and Challenges Full Text Available At
Full text available at: http://dx.doi.org/10.1561/1000000005 FPGA Architecture: Survey and Challenges Full text available at: http://dx.doi.org/10.1561/1000000005 FPGA Architecture: Survey and Challenges Ian Kuon University of Toronto Toronto, ON Canada [email protected] Russell Tessier University of Massachusetts Amherst, MA USA [email protected] Jonathan Rose University of Toronto Toronto, ON Canada [email protected] Boston – Delft Full text available at: http://dx.doi.org/10.1561/1000000005 Foundations and Trends R in Electronic Design Automation Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 USA Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is I. Kuon, R. Tessier and J. Rose, FPGA Architecture: Survey and Challenges, Foundations and Trends R in Elec- tronic Design Automation, vol 2, no 2, pp 135–253, 2007 ISBN: 978-1-60198-126-4 c 2008 I. Kuon, R. Tessier and J. Rose All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Cen- ter, Inc., 222 Rosewood Drive, Danvers, MA 01923. Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted by now Publishers Inc for users registered with the Copyright Clearance Center (CCC). -
Hardware Platforms for Embedded Computing Graphics: © Alexandra Nolte, Gesine Marwedel, 2003 Universität Dortmund Importance of Energy Efficiency
Universität Dortmund Hardware Platforms for Embedded Computing Graphics: © Alexandra Nolte, Gesine Marwedel, 2003 Universität Dortmund Importance of Energy Efficiency Efficient software design needed, otherwise, the price for software flexibility cannot be paid. © Hugo De Man (IMEC) Philips, 2007 Universität Dortmund Embedded vs. general-purpose processors • Embedded processors may be optimized for a category of applications. – Customization may be narrow or broad. • We may judge embedded processors using different metrics: – Code size. – Memory system performance. – Preditability. • Disappearing distinction: embedded processors everywhere Universität Dortmund Microcontroller Architectures Memory 0 Address Bus Program + CPU Data Bus Data Von Neumann 2n Architecture Memory 0 Address Bus Program CPU Fetch Bus Harvard Address Bus 0 Architecture Data Bus Data Universität Dortmund RISC processors • RISC generally means highly-pipelinable, one instruction per cycle. • Pipelines of embedded RISC processors have grown over time: – ARM7 has 3-stage pipeline. – ARM9 has 5-stage pipeline. – ARM11 has eight-stage pipeline. ARM11 pipeline [ARM05]. Universität Dortmund ARM Cortex Based on ARMv7 Architecture & Thumb®-2 ISA – ARM Cortex A Series - Applications CPUs focused on the execution of complex OS and user applications • First Product: Cortex-A8 • Executes ARM, Thumb-2 instructions – ARM Cortex R Series - Deeply embedded processors focused on Real-time environments • First Product: Cortex-R4(F) • Executes ARM, Thumb-2 instructions – ARM Cortex M -
Reconfigurable Computing
Reconfigurable Computing David Boland1, Chung-Kuan Cheng2, Andrew B. Kahng2, Philip H.W. Leong1 1School of Electrical and Information Engineering, The University of Sydney, Australia 2006 2Dept. of Computer Science and Engineering, University of California, La Jolla, California Abstract: Reconfigurable computing is the application of adaptable fabrics to solve computational problems, often taking advantage of the flexibility available to produce problem-specific architectures that achieve high performance because of customization. Reconfigurable computing has been successfully applied to fields as diverse as digital signal processing, cryptography, bioinformatics, logic emulation, CAD tool acceleration, scientific computing, and rapid prototyping. Although Estrin-first proposed the idea of a reconfigurable system in the form of a fixed plus variable structure computer in 1960 [1] it has only been in recent years that reconfigurable fabrics, in the form of field-programmable gate arrays (FPGAs), have reached sufficient density to make them a compelling implementation platform for high performance applications and embedded systems. In this article, intended for the non-specialist, we describe some of the basic concepts, tools and architectures associated with reconfigurable computing. Keywords: reconfigurable computing; adaptable fabrics; application integrated circuits; field programmable gate arrays (FPGAs); system architecture; runtime 1 Introduction Although reconfigurable fabrics can in principle be constructed from any type of technology, in practice, most contemporary designs are made using commercial field programmable gate arrays (FPGAs). An FPGA is an integrated circuit containing an array of logic gates in which the connections can be configured by downloading a bitstream to its memory. FPGAs can also be embedded in integrated circuits as intellectual property cores. -
Xilinx Vivado – „EDK” Embedded Development) 4
EFOP-3.4.3-16-2016-00009 A fels őfokú oktatás min őségének és hozzáférhet őségének együttes javítása a Pannon Egyetemen EMBEDDED SYSTEM DEVELOPMENT (MISAM154R) Created by Zsolt Voroshazi, PhD [email protected] Updated: 02 March. 2020. 2. FPGAS & PLATFORMS Embedded Systems Topics covered 1. Introduction – Embedded Systems 2. FPGAs, Digilent ZyBo development platform 3. Embedded System - Firmware development environment (Xilinx Vivado – „EDK” Embedded Development) 4. Embedded System - Software development environment (Xilinx VITIS – „SDK”) 5. Embedded Base System Build (and Board Bring-Up) 6. Adding Peripherals (from IP database) to BSB 7. Adding Custom (=own) Peripherals to BSB 8. Design and Development of Complex IP cores and applications (e.g. camera/video/ audio controllers) 3 Further references • XILINX official website: http://www.xilinx.com • EE Journal – Electronic Engineering: http://www.eejournal.com/design/embedded • EE Times - News: http://www.eetimes.com/design/embedded 4 PLD & FPGA CIRCUITS General description PAST … • Before ’80s, designing logic networks for digital circuits, modern development tools were not yet available as today. • The design of high complexity (multi I/O) logical combination and sequential networks was therefore slow and cumbersome, often coupled with paper- based design, multiple manual checks, and calculations. • We could not talk about advanced design and simulation tools (CAD) either, so there was a high probability of error in a prototype design. 6 … AND PRESENT • Today, all of these are available in an automated way (EDA - Electronic Design Automation), which, in addition to the use of Programmable Logic Devices (PLD), is relatively fast for both Printed Circuit Boards (PCB) and Application-specific Integrated Circuits and Processors (ASIC / ASSP). -
A Reconfigurable Convolutional Neural Network-Accelerated
electronics Article A Reconfigurable Convolutional Neural Network-Accelerated Coprocessor Based on RISC-V Instruction Set Ning Wu *, Tao Jiang, Lei Zhang, Fang Zhou and Fen Ge College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; [email protected] (T.J.); [email protected] (L.Z.); [email protected] (F.Z.); [email protected] (F.G.) * Correspondence: [email protected]; Tel.: +86-139-5189-3307 Received: 5 May 2020; Accepted: 8 June 2020; Published: 16 June 2020 Abstract: As a typical artificial intelligence algorithm, the convolutional neural network (CNN) is widely used in the Internet of Things (IoT) system. In order to improve the computing ability of an IoT CPU, this paper designs a reconfigurable CNN-accelerated coprocessor based on the RISC-V instruction set. The interconnection structure of the acceleration chain designed by the predecessors is optimized, and the accelerator is connected to the RISC-V CPU core in the form of a coprocessor. The corresponding instruction of the coprocessor is designed and the instruction compiling environment is established. Through the inline assembly in the C language, the coprocessor instructions are called, coprocessor acceleration library functions are established, and common algorithms in the IoT system are implemented on the coprocessor. Finally, resource consumption evaluation and performance analysis of the coprocessor are completed on a Xilinx FPGA. The evaluation results show that the reconfigurable CNN-accelerated coprocessor only consumes 8534 LUTS, accounting for 47.6% of the total SoC system. The number of instruction cycles required to implement functions such as convolution and pooling based on the designed coprocessor instructions is better than using the standard instruction set, and the acceleration ratio of convolution is 6.27 times that of the standard instruction set. -
Stratix II Vs. Virtex-4 Power Comparison & Estimation Accuracy
White Paper Stratix II vs. Virtex-4 Power Comparison & Estimation Accuracy Introduction This document compares power consumption and power estimation accuracy for Altera® Stratix® II FPGAs and Xilinx Virtex-4 FPGAs. The comparison addresses all components of power: core dynamic power, core static power, and I/O power. This document uses bench-measured results to compare actual dynamic power consumption. To compare power estimation accuracy, the analysis uses the vendor-recommended power estimation software tools. The summary of these comparisons are: Altera’s Quartus® II PowerPlay power analyzer tool is accurate (to within 20%), while Xilinx’s tools are significantly less accurate. Stratix II devices exhibit lower dynamic power than Virtex-4 devices, resulting in total device power that is equal. Having an accurate FPGA power estimate is important to avoid surprises late in the design and prototyping phase. Inaccurate estimates can be costly and cause design issues, including: board re-layout, changes to power-management circuitry, changes cooling solution, unreliable FPGA operation, undue heating of other components, and changes to the FPGA design. Furthermore, without accurate power estimates, it is impossible for the designer and FPGA CAD software to optimize design power. This white paper contains the following sections: Components of total device power Power estimation and measurement methodology Core dynamic power comparison – power tool accuracy and bench measurements Core Static power comparison I/O power comparison Total device power summary For competitive comparisons on performance and density between Stratix II and Virtex-4 devices, refer to the following white papers from the Altera web site: Stratix II vs. -
Reconfigurable Computing Brittany Ransbottom Benjamin Wheeler What Is Reconfigurable Computing?
Reconfigurable Computing Brittany Ransbottom Benjamin Wheeler What is Reconfigurable Computing? A computer architecture style which tries to find a happy medium of general purpose processors and ASICs through the use of the flexibility of software, and the speed of hardware. Reconfigurable Computing von Neumann vs. Reconfigurable Fixed resources Variable resources The architecture is While the designed prior to architecture is use as a processor designed prior to use, it can be adjusted or reconfigured to use different resources Execution in SW Execution in HW Spatial vs. Temporal Hardware Software Software The Reconfigurable Computing Paradox • Migration from software to configware o speed-up factors and electricity consumption reduction of about four orders of magnitude o Clock frequency and other specifications of FPGAs are behind microprocessors by about four orders of magnitude Current State No designs merge GPP and ASIC enough to be marketable Too specialized (a glorified asic), or priced too high to benefit replacing GPPs Coarse-Grained doesn't have SW support necessary What is an FPGA? Reprogrammable Hardware Generally configured in VHDL or Verilog Can execute processes spatially as opposed to temporally The FPGA Coprocessor Option +Reprogrammable Logic -Requires pre-existing HW design -Needs to be specific processing, or have an intensive controller/compiler What is Coarse-grained computing Functional units (add, subtraction, multiplication - word-level operations) interconnected in a mesh style Coarse-Grained +Short Reconfiguration