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ASIC Implemented Microblaze-Based Coprocessor for Data Stream
ASIC-IMPLEMENTED MICROBLAZE-BASED COPROCESSOR FOR DATA STREAM MANAGEMENT SYSTEMS A Thesis Submitted to the Faculty of Purdue University by Linknath Surya Balasubramanian In Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical and Computer Engineering May 2020 Purdue University Indianapolis, Indiana ii THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF THESIS APPROVAL Dr. John J. Lee, Chair Department of Electrical and Computer Engineering Dr. Lauren A. Christopher Department of Electrical and Computer Engineering Dr. Maher E. Rizkalla Department of Electrical and Computer Engineering Approved by: Dr. Brian King Head of Graduate Program iii ACKNOWLEDGMENTS I would first like to express my gratitude to my advisor Dr. John J. Lee and my thesis committee members Dr. Lauren A. Christopher and Dr. Maher E. Rizkalla for their patience, guidance, and support during this journey. I would also like to thank Mrs. Sherrie Tucker for her patience, help, and encouragement. Lastly, I must thank Dr. Pranav Vaidya and Mr. Tareq S. Alqaisi for all their support, technical guidance, and advice. Thank you all for taking time and helping me complete this study. iv TABLE OF CONTENTS Page LIST OF TABLES :::::::::::::::::::::::::::::::::: vi LIST OF FIGURES ::::::::::::::::::::::::::::::::: vii ABSTRACT ::::::::::::::::::::::::::::::::::::: ix 1 INTRODUCTION :::::::::::::::::::::::::::::::: 1 1.1 Previous Work ::::::::::::::::::::::::::::::: 1 1.2 Motivation :::::::::::::::::::::::::::::::::: 2 1.3 Thesis Outline :::::::::::::::::::::::::::::::: -
The Intro to GPGPU CPU Vs
12/12/11! The Intro to GPGPU . Dr. Chokchai (Box) Leangsuksun, PhD! Louisiana Tech University. Ruston, LA! ! CPU vs. GPU • CPU – Fast caches – Branching adaptability – High performance • GPU – Multiple ALUs – Fast onboard memory – High throughput on parallel tasks • Executes program on each fragment/vertex • CPUs are great for task parallelism • GPUs are great for data parallelism Supercomputing 20082 Education Program 1! 12/12/11! CPU vs. GPU - Hardware • More transistors devoted to data processing CUDA programming guide 3.1 3 CPU vs. GPU – Computation Power CUDA programming guide 3.1! 2! 12/12/11! CPU vs. GPU – Memory Bandwidth CUDA programming guide 3.1! What is GPGPU ? • General Purpose computation using GPU in applications other than 3D graphics – GPU accelerates critical path of application • Data parallel algorithms leverage GPU attributes – Large data arrays, streaming throughput – Fine-grain SIMD parallelism – Low-latency floating point (FP) computation © David Kirk/NVIDIA and Wen-mei W. Hwu, 2007! ECE 498AL, University of Illinois, Urbana-Champaign! 3! 12/12/11! Why is GPGPU? • Large number of cores – – 100-1000 cores in a single card • Low cost – less than $100-$1500 • Green computing – Low power consumption – 135 watts/card – 135 w vs 30000 w (300 watts * 100) • 1 card can perform > 100 desktops 12/14/09!– $750 vs 50000 ($500 * 100) 7 Two major players 4! 12/12/11! Parallel Computing on a GPU • NVIDIA GPU Computing Architecture – Via a HW device interface – In laptops, desktops, workstations, servers • Tesla T10 1070 from 1-4 TFLOPS • AMD/ATI 5970 x2 3200 cores • NVIDIA Tegra is an all-in-one (system-on-a-chip) ATI 4850! processor architecture derived from the ARM family • GPU parallelism is better than Moore’s law, more doubling every year • GPGPU is a GPU that allows user to process both graphics and non-graphics applications. -
System-On-A-Chip (Soc) & ARM Architecture
System-on-a-Chip (SoC) & ARM Architecture EE2222 Computer Interfacing and Microprocessors Partially based on System-on-Chip Design by Hao Zheng 2020 EE2222 1 Overview • A system-on-a-chip (SoC): • a computing system on a single silicon substrate that integrates both hardware and software. • Hardware packages all necessary electronics for a particular application. • which implemented by SW running on HW. • Aim for low power and low cost. • Also more reliable than multi-component systems. 2020 EE2222 2 Driven by semiconductor advances 2020 EE2222 3 Basic SoC Model 2020 EE2222 4 2020 EE2222 5 SoC vs Processors System on a chip Processors on a chip processor multiple, simple, heterogeneous few, complex, homogeneous cache one level, small 2-3 levels, extensive memory embedded, on chip very large, off chip functionality special purpose general purpose interconnect wide, high bandwidth often through cache power, cost both low both high operation largely stand-alone need other chips 2020 EE2222 6 Embedded Systems • 98% processors sold annually are used in embedded applications. 2020 EE2222 7 Embedded Systems: Design Challenges • Power/energy efficient: • mobile & battery powered • Highly reliable: • Extreme environment (e.g. temperature) • Real-time operations: • predictable performance • Highly complex • A modern automobile with 55 electronic control units • Tightly coupled Software & Hardware • Rapid development at low price 2020 EE2222 8 EECS222A: SoC Description and Modeling Lecture 1 Design Complexity Challenge Design• Productivity Complexity -
Comparative Study of Various Systems on Chips Embedded in Mobile Devices
Innovative Systems Design and Engineering www.iiste.org ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online) Vol.4, No.7, 2013 - National Conference on Emerging Trends in Electrical, Instrumentation & Communication Engineering Comparative Study of Various Systems on Chips Embedded in Mobile Devices Deepti Bansal(Assistant Professor) BVCOE, New Delhi Tel N: +919711341624 Email: [email protected] ABSTRACT Systems-on-chips (SoCs) are the latest incarnation of very large scale integration (VLSI) technology. A single integrated circuit can contain over 100 million transistors. Harnessing all this computing power requires designers to move beyond logic design into computer architecture, meet real-time deadlines, ensure low-power operation, and so on. These opportunities and challenges make SoC design an important field of research. So in the paper we will try to focus on the various aspects of SOC and the applications offered by it. Also the different parameters to be checked for functional verification like integration and complexity are described in brief. We will focus mainly on the applications of system on chip in mobile devices and then we will compare various mobile vendors in terms of different parameters like cost, memory, features, weight, and battery life, audio and video applications. A brief discussion on the upcoming technologies in SoC used in smart phones as announced by Intel, Microsoft, Texas etc. is also taken up. Keywords: System on Chip, Core Frame Architecture, Arm Processors, Smartphone. 1. Introduction: What Is SoC? We first need to define system-on-chip (SoC). A SoC is a complex integrated circuit that implements most or all of the functions of a complete electronic system. -
System-On-Chip Design with Virtual Components
past designs can a huge chip be com- pleted within a reasonable time. This FEATURE solution usually entails reusing designs from previous generations of products ARTICLE and often leverages design work done by other groups in the same company. Various forms of intercompany cross licensing and technology sharing Thomas Anderson can provide access to design technol- ogy that may be reused in new ways. Many large companies have estab- lished central organizations to pro- mote design reuse and sharing, and to System-on-Chip Design look for external IP sources. One challenge faced by IP acquisi- tion teams is that many designs aren’t well suited for reuse. Designing with with Virtual Components reuse in mind requires extra time and effort, and often more logic as well— requirements likely to be at odds with the time-to-market goals of a product design team. Therefore, a merchant semiconduc- tor IP industry has arisen to provide designs that were developed specifically for reuse in a wide range of applications. These designs are backed by documen- esign reuse for tation and support similar to that d semiconductor provided by a semiconductor supplier. Here in the Recycling projects has evolved The terms “virtual component” from an interesting con- and “core” commonly denote reusable Age, designing for cept to a requirement. Today’s huge semiconductor IP that is offered for system-on-a-chip (SOC) designs rou- license as a product. The latter term is reuse may sound like tinely require millions of transistors. promoted extensively by the Virtual Silicon geometry continues to shrink Socket Interface (VSI) Alliance, a joint a great idea. -
Lecture Notes
Lecture #4-5: Computer Hardware (Overview and CPUs) CS106E Spring 2018, Young In these lectures, we begin our three-lecture exploration of Computer Hardware. We start by looking at the different types of computer components and how they interact during basic computer operations. Next, we focus specifically on the CPU (Central Processing Unit). We take a look at the Machine Language of the CPU and discover it’s really quite primitive. We explore how Compilers and Interpreters allow us to go from the High-Level Languages we are used to programming to the Low-Level machine language actually used by the CPU. Most modern CPUs are multicore. We take a look at when multicore provides big advantages and when it doesn’t. We also take a short look at Graphics Processing Units (GPUs) and what they might be used for. We end by taking a look at Reduced Instruction Set Computing (RISC) and Complex Instruction Set Computing (CISC). Stanford President John Hennessy won the Turing Award (Computer Science’s equivalent of the Nobel Prize) for his work on RISC computing. Hardware and Software: Hardware refers to the physical components of a computer. Software refers to the programs or instructions that run on the physical computer. - We can entirely change the software on a computer, without changing the hardware and it will transform how the computer works. I can take an Apple MacBook for example, remove the Apple Software and install Microsoft Windows, and I now have a Window’s computer. - In the next two lectures we will focus entirely on Hardware. -
EE Concentration: System-On-A-Chip (Soc)
EE Concentration: System-on-a-Chip (SoC) Requirements: Complete ESE350, ESE370, CIS371, ESE532 Requirement Flow: Impact: The chip at the heart of your smartphone, tablet, or mp3 player (including the Apple A11, A12) is an SoC. The chips that run almost all of your gadgets today are SoCs. These are the current culmination of miniaturization and part count reduction that allows such systems to built inexpensively and from small part counts. These chips democratize innovation, by providing a platform for the deployment of novel ideas without requiring hundreds of millions of dollars to build new custom ICs. Description: Modern computational and control chips contain billions of transistors and run software that has millions of lines of code. They integrate complete systems including multiple, potentially heterogeneous, processing elements, sophisticated memory hierarchies, communications, and rich interfaces for inputs and outputs including sensing and actuations. To design these systems, engineers must understand IC technology, digital circuits, processor and accelerator architectures, networking, and composition and interfacing and be able to manage hardware/software trade-offs. This concentration prepares students both to participate in the design of these SoC architectures and to use SoC architectures as implementation vehicles for novel embedded computing tasks. Sample industries and companies: ● Integrated Circuit Design: ARM, IBM, Intel, Nvidia, Samsung, Qualcomm, Xilinx ● Consumer Electronics: Apple, Samsung, NEST, Hewlett Packard ● Systems: Amazon, CISCO, Google, Facebook, Microsoft ● Automotive and Aerospace: Boeing, Ford, Space-X, Tesla, Waymo ● Your startup Sample Job Titles: ● Hardware Engineer, Chip Designer, Chip Architect, Architect, Verification Engineer, Software Engineering, Embedded Software Engineer, Member of Technical Staff, VP Engineering, CTO Graduate research in: computer systems and architecture . -
Threading SIMD and MIMD in the Multicore Context the Ultrasparc T2
Overview SIMD and MIMD in the Multicore Context Single Instruction Multiple Instruction ● (note: Tute 02 this Weds - handouts) ● Flynn’s Taxonomy Single Data SISD MISD ● multicore architecture concepts Multiple Data SIMD MIMD ● for SIMD, the control unit and processor state (registers) can be shared ■ hardware threading ■ SIMD vs MIMD in the multicore context ● however, SIMD is limited to data parallelism (through multiple ALUs) ■ ● T2: design features for multicore algorithms need a regular structure, e.g. dense linear algebra, graphics ■ SSE2, Altivec, Cell SPE (128-bit registers); e.g. 4×32-bit add ■ system on a chip Rx: x x x x ■ 3 2 1 0 execution: (in-order) pipeline, instruction latency + ■ thread scheduling Ry: y3 y2 y1 y0 ■ caches: associativity, coherence, prefetch = ■ memory system: crossbar, memory controller Rz: z3 z2 z1 z0 (zi = xi + yi) ■ intermission ■ design requires massive effort; requires support from a commodity environment ■ speculation; power savings ■ massive parallelism (e.g. nVidia GPGPU) but memory is still a bottleneck ■ OpenSPARC ● multicore (CMT) is MIMD; hardware threading can be regarded as MIMD ● T2 performance (why the T2 is designed as it is) ■ higher hardware costs also includes larger shared resources (caches, TLBs) ● the Rock processor (slides by Andrew Over; ref: Tremblay, IEEE Micro 2009 ) needed ⇒ less parallelism than for SIMD COMP8320 Lecture 2: Multicore Architecture and the T2 2011 ◭◭◭ • ◮◮◮ × 1 COMP8320 Lecture 2: Multicore Architecture and the T2 2011 ◭◭◭ • ◮◮◮ × 3 Hardware (Multi)threading The UltraSPARC T2: System on a Chip ● recall concurrent execution on a single CPU: switch between threads (or ● OpenSparc Slide Cast Ch 5: p79–81,89 processes) requires the saving (in memory) of thread state (register values) ● aggressively multicore: 8 cores, each with 8-way hardware threading (64 virtual ■ motivation: utilize CPU better when thread stalled for I/O (6300 Lect O1, p9–10) CPUs) ■ what are the costs? do the same for smaller stalls? (e.g. -
A Soft Processor Microblaze-Based Embedded System for Cardiac Monitoring
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 9, 2013 A Soft Processor MicroBlaze-Based Embedded System for Cardiac Monitoring El Hassan El Mimouni Mohammed Karim University Sidi Mohammed Ben Abdellah University Sidi Mohammed Ben Abdellah Fès, Morocco Fès, Morocco Abstract—this paper aims to contribute to the efforts of recent years, and has become also an important way to assert design community to demonstrate the effectiveness of the state of the heart’s condition [5 - 9]. the art Field Programmable Gate Array (FPGA), in the embedded systems development, taking a case study in the II. SYSTEM OVERVIEW biomedical field. With this design approach, we have developed a We have designed and implemented a prototype of basic System on Chip (SoC) for cardiac monitoring based on the soft embedded system for cardiac monitoring, whose functional processor MicroBlaze and the Xilkernel Real Time Operating block diagram is shown in the figure 2; it exhibits a modular System (RTOS), both from Xilinx. The system permits the structure that facilitates the development and debugging. Thus, acquisition and the digitizing of the Electrocardiogram (ECG) analog signal, displaying heart rate on seven segments module it includes 2 main modules: and ECG on Video Graphics Adapter (VGA) screen, tracing the An analog module intended for acquiring and heart rate variability (HRV) tachogram, and communication conditioning of the analog ECG signal to make it with a Personal Computer (PC) via the serial port. We have used appropriate for use by the second digital module ; the MIT_BIH Database records to test and evaluate our implementation performance. -
Performance Evaluation of FPGA Based Embedded ARM Processor
10.1109/ULTSYM.2013.0135 Performance Evaluation of FPGA based Embedded ARM Processor for Ultrasonic Imaging Spenser Gilliland, Pramod Govindan, Thomas Gonnot and Jafar Saniie Department of Electrical and Computer Engineering Illinois Institute of Technology, Chicago IL, U.S.A. Abstract- This study evaluates the performance of an FPGA based embedded ARM processor system to implement signal processing for ultrasonic imaging and nondestructive testing applications. FPGA based embedded processors possess many advantages including a reduced overall development time, increased performance, and the ability to perform hardware- software (HW/SW) co-design. This study examines the execution performance of split spectrum processing, chirplet signal decomposition, Wigner-Ville distributions and short time Fourier transform implementations, on two embedded processing platforms: a Xilinx Virtex-5 FPGA with embedded MicroBlaze processor and a Xilinx Zynq FPGA with embedded ARM processor. Overall, the Xilinx Zynq FPGA significantly outperforms the Virtex-5 based system in software applications I. INTRODUCTION Figure 1. RUSH SoC setup Generally, ultrasonic imaging applications use personal computers or hand held devices. As these devices are not frequency diverse flaw detection [1], parametric echo specifically designed for efficiently executing estimation [2], and joint time-frequency distribution [3]) on computationally intensive ultrasonic signal processing the RUSH platform using a Xilinx Virtex-5 FPGA with an algorithms, the performance of these applications can be embedded soft-core MicroBlaze processor [4,5] and a improved by executing the algorithms using a dedicated Xilinx Zynq 7020 FPGA with an embedded ARM processor embedded system-on-chip (SoC) hardware. However, [6,7] as shown in Figure 2. porting these applications onto an embedded system requires deep knowledge of the processor architecture and RUSH embedded software development tools. -
Chapter 5: Asics Vs. Plds
Chapter 5: ASICs Vs. PLDs 5.1 Introduction A general definition of the term Application Specific Integrated Circuit (ASIC) is virtually every type of chip that is designed to perform a dedicated task. ASICS, more specifically, are designed by the end user to perform some proprietary application. Semi- custom and full-custom Application Specific Integrated Circuits are very useful in integrating digital, analog, mixed signal or system-on-a-chip (SOC) designs but are very costly and not schedule friendly. Depending on the design application, there are many advantages in using ASICs rather than Field Programmable Gate Arrays (FPGAs) or Complex Programmable Logic devices (CPLDs). Some advantages include higher performance, increased densities and decreased space requirements. Some disadvantages include lacking flexibility for changes and difficulty to test and debug. There are some design applications best suited for ASIC technology and others suited for PLDs. Logic designs done in FPGA occupy more space and have decreased performance and may need to be migrated to an ASIC methodology. The migration process introduces issues such as architectural difference and logic mapping to vendor specified functions. 5.2 ASIC Industry The ASIC industry is very volatile with new companies, products and methodologies emerging daily. In the mid-1980s the prediction was that ASIC designs would be taking over 50% of the electronic design market by 1990. When 1990 came the ASIC market turned out to be approximately 10%. Most of the focus for ASICS is providing a technology capable of handling 100,000 or more gates with very high performance. Most of the new ASIC designs do not require high density and 79 performance. -
Evaluation of Synthesizable CPU Cores
Evaluation of synthesizable CPU cores DANIEL MATTSSON MARCUS CHRISTENSSON Maste r ' s Thesis Com p u t e r Science an d Eng i n ee r i n g Pro g r a m CHALMERS UNIVERSITY OF TECHNOLOGY Depart men t of Computer Engineering Gothe n bu r g 20 0 4 All rights reserved. This publication is protected by law in accordance with “Lagen om Upphovsrätt, 1960:729”. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the authors. Daniel Mattsson and Marcus Christensson, Gothenburg 2004. Evaluation of synthesizable CPU cores Abstract The three synthesizable processors: LEON2 from Gaisler Research, MicroBlaze from Xilinx, and OpenRISC 1200 from OpenCores are evaluated and discussed. Performance in terms of benchmark results and area resource usage is measured. Different aspects like usability and configurability are also reviewed. Three configurations for each of the processors are defined and evaluated: the comparable configuration, the performance optimized configuration and the area optimized configuration. For each of the configurations three benchmarks are executed: the Dhrystone 2.1 benchmark, the Stanford benchmark suite and a typical control application run as a benchmark. A detailed analysis of the three processors and their development tools is presented. The three benchmarks are described and motivated. Conclusions and results in terms of benchmark results, performance per clock cycle and performance per area unit are discussed and presented. Sammanfattning De tre syntetiserbara processorerna: LEON2 från Gaisler Research, MicroBlaze från Xilinx och OpenRISC 1200 från OpenCores utvärderas och diskuteras.