FPGA Implementation of RISC-Based Memory-Centric Processor Architecture
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Chapter 5 Multiprocessors and Thread-Level Parallelism
Computer Architecture A Quantitative Approach, Fifth Edition Chapter 5 Multiprocessors and Thread-Level Parallelism Copyright © 2012, Elsevier Inc. All rights reserved. 1 Contents 1. Introduction 2. Centralized SMA – shared memory architecture 3. Performance of SMA 4. DMA – distributed memory architecture 5. Synchronization 6. Models of Consistency Copyright © 2012, Elsevier Inc. All rights reserved. 2 1. Introduction. Why multiprocessors? Need for more computing power Data intensive applications Utility computing requires powerful processors Several ways to increase processor performance Increased clock rate limited ability Architectural ILP, CPI – increasingly more difficult Multi-processor, multi-core systems more feasible based on current technologies Advantages of multiprocessors and multi-core Replication rather than unique design. Copyright © 2012, Elsevier Inc. All rights reserved. 3 Introduction Multiprocessor types Symmetric multiprocessors (SMP) Share single memory with uniform memory access/latency (UMA) Small number of cores Distributed shared memory (DSM) Memory distributed among processors. Non-uniform memory access/latency (NUMA) Processors connected via direct (switched) and non-direct (multi- hop) interconnection networks Copyright © 2012, Elsevier Inc. All rights reserved. 4 Important ideas Technology drives the solutions. Multi-cores have altered the game!! Thread-level parallelism (TLP) vs ILP. Computing and communication deeply intertwined. Write serialization exploits broadcast communication -
System Design for a Computational-RAM Logic-In-Memory Parailel-Processing Machine
System Design for a Computational-RAM Logic-In-Memory ParaIlel-Processing Machine Peter M. Nyasulu, B .Sc., M.Eng. A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy Ottaw a-Carleton Ins titute for Eleceical and Computer Engineering, Department of Electronics, Faculty of Engineering, Carleton University, Ottawa, Ontario, Canada May, 1999 O Peter M. Nyasulu, 1999 National Library Biôiiothkque nationale du Canada Acquisitions and Acquisitions et Bibliographie Services services bibliographiques 39S Weiiington Street 395. nie WeUingtm OnawaON KlAW Ottawa ON K1A ON4 Canada Canada The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, ban, distribute or seU reproduire, prêter, distribuer ou copies of this thesis in microform, vendre des copies de cette thèse sous paper or electronic formats. la forme de microficbe/nlm, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fkom it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Abstract Integrating several 1-bit processing elements at the sense amplifiers of a standard RAM improves the performance of massively-paralle1 applications because of the inherent parallelism and high data bandwidth inside the memory chip. -
Computer Organization & Architecture Eie
COMPUTER ORGANIZATION & ARCHITECTURE EIE 411 Course Lecturer: Engr Banji Adedayo. Reg COREN. The characteristics of different computers vary considerably from category to category. Computers for data processing activities have different features than those with scientific features. Even computers configured within the same application area have variations in design. Computer architecture is the science of integrating those components to achieve a level of functionality and performance. It is logical organization or designs of the hardware that make up the computer system. The internal organization of a digital system is defined by the sequence of micro operations it performs on the data stored in its registers. The internal structure of a MICRO-PROCESSOR is called its architecture and includes the number lay out and functionality of registers, memory cell, decoders, controllers and clocks. HISTORY OF COMPUTER HARDWARE The first use of the word ‘Computer’ was recorded in 1613, referring to a person who carried out calculation or computation. A brief History: Computer as we all know 2day had its beginning with 19th century English Mathematics Professor named Chales Babage. He designed the analytical engine and it was this design that the basic frame work of the computer of today are based on. 1st Generation 1937-1946 The first electronic digital computer was built by Dr John V. Atanasoff & Berry Cliford (ABC). In 1943 an electronic computer named colossus was built for military. 1946 – The first general purpose digital computer- the Electronic Numerical Integrator and computer (ENIAC) was built. This computer weighed 30 tons and had 18,000 vacuum tubes which were used for processing. -
Parallel Computer Architecture
Parallel Computer Architecture Introduction to Parallel Computing CIS 410/510 Department of Computer and Information Science Lecture 2 – Parallel Architecture Outline q Parallel architecture types q Instruction-level parallelism q Vector processing q SIMD q Shared memory ❍ Memory organization: UMA, NUMA ❍ Coherency: CC-UMA, CC-NUMA q Interconnection networks q Distributed memory q Clusters q Clusters of SMPs q Heterogeneous clusters of SMPs Introduction to Parallel Computing, University of Oregon, IPCC Lecture 2 – Parallel Architecture 2 Parallel Architecture Types • Uniprocessor • Shared Memory – Scalar processor Multiprocessor (SMP) processor – Shared memory address space – Bus-based memory system memory processor … processor – Vector processor bus processor vector memory memory – Interconnection network – Single Instruction Multiple processor … processor Data (SIMD) network processor … … memory memory Introduction to Parallel Computing, University of Oregon, IPCC Lecture 2 – Parallel Architecture 3 Parallel Architecture Types (2) • Distributed Memory • Cluster of SMPs Multiprocessor – Shared memory addressing – Message passing within SMP node between nodes – Message passing between SMP memory memory nodes … M M processor processor … … P … P P P interconnec2on network network interface interconnec2on network processor processor … P … P P … P memory memory … M M – Massively Parallel Processor (MPP) – Can also be regarded as MPP if • Many, many processors processor number is large Introduction to Parallel Computing, University of Oregon, -
A Modern Primer on Processing in Memory
A Modern Primer on Processing in Memory Onur Mutlua,b, Saugata Ghoseb,c, Juan Gomez-Luna´ a, Rachata Ausavarungnirund SAFARI Research Group aETH Z¨urich bCarnegie Mellon University cUniversity of Illinois at Urbana-Champaign dKing Mongkut’s University of Technology North Bangkok Abstract Modern computing systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in computing that cause performance, scalability and energy bottlenecks: (1) data access is a key bottleneck as many important applications are increasingly data-intensive, and memory bandwidth and energy do not scale well, (2) energy consumption is a key limiter in almost all computing platforms, especially server and mobile systems, (3) data movement, especially off-chip to on-chip, is very expensive in terms of bandwidth, energy and latency, much more so than computation. These trends are especially severely-felt in the data-intensive server and energy-constrained mobile systems of today. At the same time, conventional memory technology is facing many technology scaling challenges in terms of reliability, energy, and performance. As a result, memory system architects are open to organizing memory in different ways and making it more intelligent, at the expense of higher cost. The emergence of 3D-stacked memory plus logic, the adoption of error correcting codes inside the latest DRAM chips, proliferation of different main memory standards and chips, specialized for different purposes (e.g., graphics, low-power, high bandwidth, low latency), and the necessity of designing new solutions to serious reliability and security issues, such as the RowHammer phenomenon, are an evidence of this trend. -
Digital Signal Processing – II
www.jntuworld.com Digital Signal Processing – 8 December 24, 2009 VIII. DSP Processors 2007 Syllabus: Introduction to programmable DSPs: Multiplier and Multiplier-Accumulator (MAC), Modified bus structures and memory access schemes in DSPs, Multiple access memory, Multiport memory, VLSI architecture, Pipelining, Special addressing modes, On-chip peripherals. Architecture of TMS 320C5X – Introduction, Bus structure, Central Arithmetic Logic Unit, Auxiliary register, Index register, Auxiliary register, Compare register, Block move address register, Parallel Logic Unit, Memory mapped registers, Program controller, Some flags in the status registers, On-chip registers, On-chip peripherals. Contents: 8.1 DSP Processors – Market 8.2 DSP Processors – Features 8.3 Multiply-and-Accumulate 8.4 Interrupts – handling incoming signal values 8.5 Fixed- and Floating-point 8.6 Real-time FIR filter example We shall use “DSP” to mean Digital Signal Processor(s) and sometimes even refer to them as DSP processors, also as programmable digital signal processors (PDSPs). This includes 1. General purpose DSPs (such as the TMS320’s of TI and DSP563’s of Motorola and others) 2. Special purpose DSPs tailored to specific applications like FFT However, the so-called programmability of DSPs mentioned above pales in comparison to that of general purpose CPUs. In what follows, for the most part, we contrast general purpose CPUs (whose strength is in general purpose programmability) with DSPs (whose strength is in high throughput, hardwired, number crunching). For a -
Computer Memory Architecture Pdf
Computer memory architecture pdf Continue The methods used to implement the electronic computer storage memory architecture describe the methods used to implement electronic computer data storage, in such a way that it is a combination of the fastest, most reliable, most durable and least expensive way to store and obtain information. Depending on the application, one of these requirements may need to be compromised to improve the other requirement. Memory architecture also explains how binary digits are converted into electrical signals and then stored in memory cells. As well as the structure of the memory cell. For example, dynamic memory is commonly used for primary storage because of the fast speed of access. However, dynamic memory must be repeatedly updated with a spike of current dozens of times per second, or stored data will disintegrate and be lost. Flash memory allows you to store for years, but it's much slower than dynamic memory, and static memory storage cells wear out when used frequently. Similarly, the data bead is often designed to meet specific needs such as serial or parallel access to data, and memory can be designed to detect parity errors or even correct errors. The earliest memory architectures are Harvard Architecture, which has two physically separate memories and data transfer paths for the program and data, and Princeton's architecture, which uses a single path of memory and data for both the program and the storage of data. Most general-purpose computers use a hybrid split-cache modified Harvard architecture, which appears to be an application program to have a clean Princeton architectural machine with gigabytes of virtual memory, but internally (for speed) it works with a cache of instructions physically separate from a data cache more like the Harvard model. -
Introduction to Computer Architecture
Introduction to Computer Architecture Mark Bull, EPCC Building Blocks of HPC Systems Four principal technologies which make up HPC systems: • Processors • to calculate • Memory • for temporary storage of data • Interconnect • so processors can talk to each other (and the outside world) • Storage • disks for storing input/output data and tapes for long term archiving of data • These talks will focus on the first two of these. 2 Processors • Basic functionality • execute instructions to perform arithmetic operations (integer and floating point) • load data from memory and store data to memory • decide which instructions to execute next • Arithmetic is performed on values in registers (local storage in the processor) • moving data between memory and registers must be done explicitly by load and store instructions • separate integer and floating point registers • typical size ~100 values • Basic characteristics: • Clock speed • Peak floating point capability 3 Processors (cont.) • Clock speed determines rate at which instructions are executed • modern chips are around 2-3 GHz • integer and floating point calculations can be done in parallel • can also have multiple issue, e.g. simultaneous add and multiply • peak flop rate is just clock rate x no. of floating point operations per clock cycle • Whole series of hardware innovations • pipelining • out-of-order execution, speculative computation • ... • Details of these become important for extracting high performance • most features are fairly generic 4 Moore’s Law • “CPU power doubles every -
Programmable Address Generation Unit for Deep Neural Network Accelerators
DEGREE PROJECT IN ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Programmable Address Generation Unit for Deep Neural Network Accelerators MUHAMMAD JAZIB KHAN KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE KTH ROYAL INSTITUTE OF TECHNOLOGY Electrical Engineering and Computer Science Programmable Address Generation Unit for Deep Neural Network Accelerators Muhammad Jazib Khan Master in Electrical Engineering Supervisor KTH: Yu Yang Examiner: Prof. Ahmed Hemani School of Electrical Engineering and Computer Science Host company: Robert Bosch GmbH Supervisor Bosch: Sebastian Vogel and Dr. Leonardo Ecco Abstract The Convolutional Neural Networks are getting more and more popular due to their applications in revolutionary technologies like Autonomous Driving, Biomedical Imaging, and Natural Language Processing. With this increase in adoption, the complexity of underlying algorithms is also increasing. This trend entails implications for the computation platforms as well, i.e. GPUs, FPGA, or ASIC based accelerators, especially for the Address Generation Unit (AGU), which is responsible for the memory access. Existing accelerators typically have Parametrizable Datapath AGUs, which have minimal adaptability towards evolution in algorithms. Hence new hardware is required for new algorithms, which is a very inefficient approach in terms of time, resources, and reusability. In this research, six algorithms with different implications for hardware are evaluated for address generation, and a fully Programmable AGU (PAGU) is presented, which can adapt to these algorithms. These algorithms are Standard, Strided, Dilated, Upsampled and Padded convolution, and MaxPooling. The proposed AGU architecture is a Very Long Instruction Word based Application Specific Instruction Processor which has specialized components like hardware counters and zero-overhead loops and a powerful Instruction Set Architecture (ISA), which can model static and dynamic constraints and affine and non-affine Address Equations. -
A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets
IEEE TRANSACTIONS ON COMPUTERS, VOL. (VOL), NO. (NO), (MONTH) (YEAR) 1 A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets Fabian Schuiki, Michael Schaffner, Frank K. Gürkaynak, and Luca Benini, Fellow, IEEE Abstract—Most investigations into near-memory hardware accelerators for deep neural networks have primarily focused on inference, while the potential of accelerating training has received relatively little attention so far. Based on an in-depth analysis of the key computational patterns in state-of-the-art gradient-based training methods, we propose an efficient near-memory acceleration engine called NTX that can be used to train state-of-the-art deep convolutional neural networks at scale. Our main contributions are: (i) a loose coupling of RISC-V cores and NTX co-processors reducing offloading overhead by 7× over previously published results; (ii) an optimized IEEE 754 compliant data path for fast high-precision convolutions and gradient propagation; (iii) evaluation of near-memory computing with NTX embedded into residual area on the Logic Base die of a Hybrid Memory Cube; and (iv) a scaling analysis to meshes of HMCs in a data center scenario. We demonstrate a 2:7× energy efficiency improvement of NTX over contemporary GPUs at 4:4× less silicon area, and a compute performance of 1:2 Tflop=s for training large state-of-the-art networks with full floating-point precision. At the data center scale, a mesh of NTX achieves above 95% parallel and energy efficiency, while providing 2:1× energy savings or 3:1× performance improvement over a GPU-based system. -
Computer Architectures & Hardware Programming 1
COMPUTER ARCHITECTURES & HARDWARE PROGRAMMING 1 Dr. Varga, Imre University of Debrecen Department of IT Systems and Networks 06 February 2020 Requirements Practice (signature, only Hardware Programming 1) Visit classes (maximum absences: 3) 2 practical tests • All tests at least 50% 1 re-take chance (covers the whole semester) Lecture (exam, CA & HP1) Written exam Multiple choice + Calculation/Programming + Essay • At least 40% Computer Architectures Warning Lexical knowledge about the slides are necessary, but not enough to pass. Understanding is necessary, that is why active participating on lectures are advantages. Other literature can be also useful. Computer Architectures Computer Science Engineering computer science… …engineering Assembly Electronics programming Computer Programming Digital architectures languages 1-2 design Physics Operating Database systems systems Embedded Signals and systems systems System programming Computer Architectures Topics Number representation, datatype implementation Essential structure and work of CPUs Modern processors Concrete processor architecture Instruction-set, programming Aspects of programming closely related to the hardware Basics of digital technology from the point of view of the hardware Assembly programming Mapping high-level programming to low-level Computer Architectures The goal of the subject Giving knowledge about hardware Creating connection between … ‚programming’ and ‚electronics’ ‚abstract’ and ‚fundamental’ knowledge Deeper understanding of high-level programming -
Choosing a DSP Processor Berkeley Design Technology, Inc
Choosing a DSP Processor Berkeley Design Technology, Inc. Introduction A second feature shared by DSP processors is the ability to complete several accesses to memory in a sin- DSP processors are microprocessors designed to per- gle instruction cycle. This allows the processor to fetch form digital signal processing—the mathematical ma- an instruction while simultaneously fetching operands nipulation of digitally represented signals. Digital signal and/or storing the result of a previous instruction to processing is one of the core technologies in rapidly memory. For example, in calculating the vector dot prod- growing application areas such as wireless communica- uct for an FIR filter, most DSP processors are able to per- tions, audio and video processing, and industrial control. form a MAC while simultaneously loading the data Along with the rising popularity of DSP applications, the sample and coefficient for the next MAC. Such single- variety of DSP-capable processors has expanded greatly cycle multiple memory accesses are often subject to since the introduction of the first commercially success- many restrictions. Typically, all but one of the memory ful DSP chips in the early 1980s. Market research firm locations accessed must reside on-chip, and multiple Forward Concepts projects that sales of DSP processors memory accesses can only take place with certain in- will total U.S. $6.2 billion in 2000, a growth of 40 per- structions. To support simultaneous access of multiple cent over 1999. With semiconductor manufacturers vy- memory locations, DSP processors provide multiple on- ing for bigger shares of this booming market, designers’ chip buses, multi-ported on-chip memories, and in some choices will broaden even further in the next few years.