Teaching Sequential Logic VHDL Models by Synthesis and Examples
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Lecture 7: Synchronous Sequential Logic
Systems I: Computer Organization and Architecture Lecture 7: Synchronous Sequential Logic Synchronous Sequential Logic • The digital circuits that we have looked at so far are combinational, depending only on the inputs. In practice, most systems have many components that contain memory elements, which require sequential logic. • A sequential circuit contains both a combinational circuit and memory elements, whose output also serves as an input for the combinational circuit. • The binary data stored in the memory elements define the state of the sequential circuit and help determine the conditions for changing the state in the memory elements. Inputs Combinational Outputs circuit Memory elements Sequential Circuits: Synchronous and Asynchronous • There are two types of sequential circuits, classified by their signal’s timing: – Synchronous sequential circuits have behavior that is defined from knowledge of its signal at discrete instants of time. – The behavior of asynchronous sequential circuits depends on the order in which input signals change and are affected at any instant of time. Synchronous Sequential Circuits • Synchronous sequential circuits need to use signal that affect memory elements at discrete time instants. • Synchronization is achieved by a timing device called a master-clock generator which generates a periodic train of clock pulses. Basic Flip-Flop Circuit Using NOR Gates 1 R(reset) Q 0 Set state Clear state 1 Q’ 0 S(set) S R Q Q’ 1 0 1 0 0 0 1 0 (after S = 1, R = 0) 0 1 0 1 0 0 0 1 (after S = 0, R = 1) 1 1 0 0 Basic -
Synthesis and Verification of Digital Circuits Using Functional Simulation and Boolean Satisfiability
Synthesis and Verification of Digital Circuits using Functional Simulation and Boolean Satisfiability by Stephen M. Plaza A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science and Engineering) in The University of Michigan 2008 Doctoral Committee: Associate Professor Igor L. Markov, Co-Chair Assistant Professor Valeria M. Bertacco, Co-Chair Professor John P. Hayes Professor Karem A. Sakallah Associate Professor Dennis M. Sylvester Stephen M. Plaza 2008 c All Rights Reserved To my family, friends, and country ii ACKNOWLEDGEMENTS I would like to thank my advisers, Professor Igor Markov and Professor Valeria Bertacco, for inspiring me to consider various fields of research and providing feedback on my projects and papers. I also want to thank my defense committee for their comments and in- sights: Professor John Hayes, Professor Karem Sakallah, and Professor Dennis Sylvester. I would like to thank Professor David Kieras for enhancing my knowledge and apprecia- tion for computer programming and providing invaluable advice. Over the years, I have been fortunate to know and work with several wonderful stu- dents. I have collaborated extensively with Kai-hui Chang and Smita Krishnaswamy and have enjoyed numerous research discussions with them and have benefited from their in- sights. I would like to thank Ian Kountanis and Zaher Andraus for our many fun discus- sions on parallel SAT. I also appreciate the time spent collaborating with Kypros Constan- tinides and Jason Blome. Although I have not formally collaborated with Ilya Wagner, I have enjoyed numerous discussions with him during my doctoral studies. I also thank my office mates Jarrod Roy, Jin Hu, and Hector Garcia. -
A Logic Synthesis Toolbox for Reducing the Multiplicative Complexity in Logic Networks
A Logic Synthesis Toolbox for Reducing the Multiplicative Complexity in Logic Networks Eleonora Testa∗, Mathias Soekeny, Heinz Riener∗, Luca Amaruz and Giovanni De Micheli∗ ∗Integrated Systems Laboratory, EPFL, Lausanne, Switzerland yMicrosoft, Switzerland zSynopsys Inc., Design Group, Sunnyvale, California, USA Abstract—Logic synthesis is a fundamental step in the real- correlates to the resistance of the function against algebraic ization of modern integrated circuits. It has traditionally been attacks [10], while the multiplicative complexity of a logic employed for the optimization of CMOS-based designs, as well network implementing that function only provides an upper as for emerging technologies and quantum computing. Recently, bound. Consequently, minimizing the multiplicative complexity it found application in minimizing the number of AND gates in of a network is important to assess the real multiplicative cryptography benchmarks represented as xor-and graphs (XAGs). complexity of the function, and therefore its vulnerability. The number of AND gates in an XAG, which is called the logic net- work’s multiplicative complexity, plays a critical role in various Second, the number of AND gates plays an important role cryptography and security protocols such as fully homomorphic in high-level cryptography protocols such as zero-knowledge encryption (FHE) and secure multi-party computation (MPC). protocols, fully homomorphic encryption (FHE), and secure Further, the number of AND gates is also important to assess multi-party computation (MPC) [11], [12], [6]. For example, the the degree of vulnerability of a Boolean function, and influences size of the signature in post-quantum zero-knowledge signatures the cost of techniques to protect against side-channel attacks. -
Logic Optimization and Synthesis: Trends and Directions in Industry
Logic Optimization and Synthesis: Trends and Directions in Industry Luca Amaru´∗, Patrick Vuillod†, Jiong Luo∗, Janet Olson∗ ∗ Synopsys Inc., Design Group, Sunnyvale, California, USA † Synopsys Inc., Design Group, Grenoble, France Abstract—Logic synthesis is a key design step which optimizes of specific logic styles and cell layouts. Embedding as much abstract circuit representations and links them to technology. technology information as possible early in the logic optimiza- With CMOS technology moving into the deep nanometer regime, tion engine is key to make advantageous logic restructuring logic synthesis needs to be aware of physical informations early in the flow. With the rise of enhanced functionality nanodevices, opportunities carry over at the end of the design flow. research on technology needs the help of logic synthesis to capture In this paper, we examine the synergy between logic synthe- advantageous design opportunities. This paper deals with the syn- sis and technology, from an industrial perspective. We present ergy between logic synthesis and technology, from an industrial technology aware synthesis methods incorporating advanced perspective. First, we present new synthesis techniques which physical information at the core optimization engine. Internal embed detailed physical informations at the core optimization engine. Experiments show improved Quality of Results (QoR) and results evidence faster timing closure and better correlation better correlation between RTL synthesis and physical implemen- between RTL synthesis and physical implementation. We elab- tation. Second, we discuss the application of these new synthesis orate on synthesis aware technology development, where logic techniques in the early assessment of emerging nanodevices with synthesis enables a fair system-level assessment on emerging enhanced functionality. -
Designing a RISC CPU in Reversible Logic
Designing a RISC CPU in Reversible Logic Robert Wille Mathias Soeken Daniel Große Eleonora Schonborn¨ Rolf Drechsler Institute of Computer Science, University of Bremen, 28359 Bremen, Germany frwille,msoeken,grosse,eleonora,[email protected] Abstract—Driven by its promising applications, reversible logic In this paper, the recent progress in the field of reversible cir- received significant attention. As a result, an impressive progress cuit design is employed in order to design a complex system, has been made in the development of synthesis approaches, i.e. a RISC CPU composed of reversible gates. Starting from implementation of sequential elements, and hardware description languages. In this paper, these recent achievements are employed a textual specification, first the core components of the CPU in order to design a RISC CPU in reversible logic that can are identified. Previously introduced approaches are applied execute software programs written in an assembler language. The next to realize the respective combinational and sequential respective combinational and sequential components are designed elements. More precisely, the combinational components are using state-of-the-art design techniques. designed using the reversible hardware description language SyReC [17], whereas for the realization of the sequential I. INTRODUCTION elements an external controller (as suggested in [16]) is utilized. With increasing miniaturization of integrated circuits, the Plugging the respective components together, a CPU design reduction of power dissipation has become a crucial issue in results which can process software programs written in an today’s hardware design process. While due to high integration assembler language. This is demonstrated in a case study, density and new fabrication processes, energy loss has sig- where the execution of a program determining Fibonacci nificantly been reduced over the last decades, physical limits numbers is simulated. -
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization Shubham Raif,6,y, Walter Lau Neton,10,y, Yukio Miyasakao,1, Xinpei Zhanga,1, Mingfei Yua,1, Qingyang Yia,1, Masahiro Fujitaa,1, Guilherme B. Manskeb,2, Matheus F. Pontesb,2, Leomar S. da Rosa Juniorb,2, Marilton S. de Aguiarb,2, Paulo F. Butzene,2, Po-Chun Chienc,3, Yu-Shan Huangc,3, Hoa-Ren Wangc,3, Jie-Hong R. Jiangc,3, Jiaqi Gud,4, Zheng Zhaod,4, Zixuan Jiangd,4, David Z. Pand,4, Brunno A. de Abreue,5,9, Isac de Souza Camposm,5,9, Augusto Berndtm,5,9, Cristina Meinhardtm,5,9, Jonata T. Carvalhom,5,9, Mateus Grellertm,5,9, Sergio Bampie,5, Aditya Lohanaf,6, Akash Kumarf,6, Wei Zengj,7, Azadeh Davoodij,7, Rasit O. Topalogluk,7, Yuan Zhoul,8, Jordan Dotzell,8, Yichi Zhangl,8, Hanyu Wangl,8, Zhiru Zhangl,8, Valerio Tenacen,10, Pierre-Emmanuel Gaillardonn,10, Alan Mishchenkoo,y, and Satrajit Chatterjeep,y aUniversity of Tokyo, Japan, bUniversidade Federal de Pelotas, Brazil, cNational Taiwan University, Taiwan, dUniversity of Texas at Austin, USA, eUniversidade Federal do Rio Grande do Sul, Brazil, fTechnische Universitaet Dresden, Germany, jUniversity of Wisconsin–Madison, USA, kIBM, USA, lCornell University, USA, mUniversidade Federal de Santa Catarina, Brazil, nUniversity of Utah, USA, oUC Berkeley, USA, pGoogle AI, USA The alphabetic characters in the superscript represent the affiliations while the digits represent the team numbers yEqual contribution. Email: [email protected], [email protected], [email protected], [email protected] Abstract—Logic synthesis is a fundamental step in hard- artificial intelligence. -
Performance Evaluation of a Signal Processing Algorithm with General-Purpose Computing on a Graphics Processing Unit
DEGREE PROJECT IN TECHNOLOGY, FIRST CYCLE, 15 CREDITS STOCKHOLM, SWEDEN 2019 Performance Evaluation of a Signal Processing Algorithm with General-Purpose Computing on a Graphics Processing Unit FILIP APPELGREN MÅNS EKELUND KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Performance Evaluation of a Signal Processing Algorithm with General-Purpose Computing on a Graphics Processing Unit Filip Appelgren and M˚ansEkelund June 5, 2019 2 Abstract Graphics Processing Units (GPU) are increasingly being used for general-purpose programming, instead of their traditional graphical tasks. This is because of their raw computational power, which in some cases give them an advantage over the traditionally used Central Processing Unit (CPU). This thesis therefore sets out to identify the performance of a GPU in a correlation algorithm, and what parameters have the greatest effect on GPU performance. The method used for determining performance was quantitative, utilizing a clock library in C++ to measure performance of the algorithm as problem size increased. Initial problem size was set to 28 and increased exponentially to 221. The results show that smaller sample sizes perform better on the serial CPU implementation but that the parallel GPU implementations start outperforming the CPU between problem sizes of 29 and 210. It became apparent that GPU's benefit from larger problem sizes, mainly because of the memory overhead costs involved with allocating and transferring data. Further, the algorithm that is under evaluation is not suited for a parallelized implementation due to a high amount of branching. Logic can lead to warp divergence, which can drastically lower performance. -
Logical Equivalence Checking of Asynchronous Circuits Using Commercial Tools
Logical Equivalence Checking of Asynchronous Circuits Using Commercial Tools Arash Saifhashemi Hsin-Ho Huang Priyanka Bhalerao Peter A. Beerel∗ Intel Corporation Electrical Engineering Yahoo Corporation Electrical Engineering Santa Clara, CA University of Southern California Sunnyvale, CA University of Southern California Email: [email protected] Los Angeles, CA Email: [email protected] Los Angeles, CA Email: [email protected] Email: [email protected] Abstract—We propose a method for logical equivalence check generally cannot be used to compare CSP with decomposed (LEC) of asynchronous circuits using commercial synchronous versions because the decomposition often introduces pipelining tools. In particular, we verify the equivalence of asynchronous that changes the allowed sequence of events at the external circuits which are modeled at the CSP-level in SystemVerilog as interface. Therefore, some researchers only check critical prop- well as circuits modeled at the micro-architectural level using con- erties on the final decomposed design [15], [16]. ditional communication library primitives. Our approach is based on a novel three-valued logic model that abstracts the detailed Our proposed approach is different from the previous work handshaking protocol and is thus agnostic to different gate-level in the following ways: first, since it is focused on CSP- implementations, making it applicable to a variety of different level designs, it is implementation-agnostic and can be used design styles. Our experimental results with commercial LEC for design flows that target various asynchronous templates. tools on a variety of computational blocks and an asynchronous Secondly, compared to [11], we explicitly support modules microprocessor demonstrate the applicability and limitations of the proposed approach. -
Lecture 34: Bonus Topics
Lecture 34: Bonus topics Philipp Koehn, David Hovemeyer December 7, 2020 601.229 Computer Systems Fundamentals Outline I GPU programming I Virtualization and containers I Digital circuits I Compilers Code examples on web page: bonus.zip GPU programming 3D graphics Rendering 3D graphics requires significant computation: I Geometry: determine visible surfaces based on geometry of 3D shapes and position of camera I Rasterization: determine pixel colors based on surface, texture, lighting A GPU is a specialized processor for doing these computations fast GPU computation: use the GPU for general-purpose computation Streaming multiprocessor I Fetches instruction (I-Cache) I Has to apply it over a vector of data I Each vector element is processed in one thread (MT Issue) I Thread is handled by scalar processor (SP) I Special function units (SFU) Flynn’s taxonomy I SISD (single instruction, single data) I uni-processors (most CPUs until 1990s) I MIMD (multi instruction, multiple data) I all modern CPUs I multiple cores on a chip I each core runs instructions that operate on their own data I SIMD (single instruction, multiple data) I Streaming Multi-Processors (e.g., GPUs) I multiple cores on a chip I same instruction executed on different data GPU architecture GPU programming I If you have an application where I Data is regular (e.g., arrays) I Computation is regular (e.g., same computation is performed on many array elements) then doing the computation on the GPU is likely to be much faster than doing the computation on the CPU I Issues: I GPU -
Verilog HDL 1
chapter 1.fm Page 3 Friday, January 24, 2003 1:44 PM Overview of Digital Design with Verilog HDL 1 1.1 Evolution of Computer-Aided Digital Design Digital circuit design has evolved rapidly over the last 25 years. The earliest digital circuits were designed with vacuum tubes and transistors. Integrated circuits were then invented where logic gates were placed on a single chip. The first integrated circuit (IC) chips were SSI (Small Scale Integration) chips where the gate count was very small. As technologies became sophisticated, designers were able to place circuits with hundreds of gates on a chip. These chips were called MSI (Medium Scale Integration) chips. With the advent of LSI (Large Scale Integration), designers could put thousands of gates on a single chip. At this point, design processes started getting very complicated, and designers felt the need to automate these processes. Electronic Design Automation (EDA)1 techniques began to evolve. Chip designers began to use circuit and logic simulation techniques to verify the functionality of building blocks of the order of about 100 transistors. The circuits were still tested on the breadboard, and the layout was done on paper or by hand on a graphic computer terminal. With the advent of VLSI (Very Large Scale Integration) technology, designers could design single chips with more than 100,000 transistors. Because of the complexity of these circuits, it was not possible to verify these circuits on a breadboard. Computer- aided techniques became critical for verification and design of VLSI digital circuits. Computer programs to do automatic placement and routing of circuit layouts also became popular. -
SEQUENTIAL LOGIC Combinational: Output Depends Only on Current Inputs Sequential: Output Depend on Current Inputs Plus Past History Includes Memory Elements
10/10/2019 COMBINATIONAL VS. SEQUENTIAL DIGITAL ELECTRONICS LOGIC SYSTEM DESIGN Combinational Circuit Sequential Circuit inputs Combinational outputs inputs Combinational outputs FALL 2019 Logic Logic PROF. IRIS BAHAR (TAUGHT BY JIWON CHOE) OCTOBER 9, 2019 State LECTURE 11: SEQUENTIAL LOGIC Combinational: Output depends only on current inputs Sequential: Output depend on current inputs plus past history Includes memory elements BISTABLE MEMORY STORAGE SEQUENTIAL CIRCUITS ELEMENT Fundamental building block of other state elements Two outputs, Q, Q Outputs depend on inputs and state variables No inputs I1 Q The state variables embody the past Q Q I2 I1 Storage elements hold the state variables A clock periodically advances the circuit I2 Q What does the circuit do? 1 10/10/2019 BISTABLE MEMORY ELEMENT REVISIT NOR & NAND GATES Controlling inputs for NAND and NOR gates 0 • Consider the two possible cases: I1 Q 1 – Q = 0: then Q’ = 1 and Q = 0 (consistent) X X 0 1 1 0 I2 Q 0 1 1 – Q = 1: then Q’ = 0 and Q = 1 (consistent) I1 Q 0 Implementing NOT with NAND/NOR using non-controlling 1 0 I2 Q inputs – Bistable circuit stores 1 bit of state in the X X X’ X’ state variable, Q (or Q’ ) 1 0 • But there are no inputs to control the state S-R (SET/RESET) LATCH S-R LATCH ANALYSIS 0 R N1 Q – S = 1, R = 0: then Q = 1 and Q = 0 . Consider the four possible cases: 1 N2 Q . S = 1, R = 0 S . S = 0, R = 1 R N1 Q 1 R . -
CPE 323 Introduction to Embedded Computer Systems: Introduction
CPE 323 Introduction to Embedded Computer Systems: Introduction Instructor: Dr Aleksandar Milenkovic CPE 323 Administration Syllabus textbook & other references grading policy important dates course outline Prerequisites Number representation Digital design: combinational and sequential logic Computer systems: organization Embedded Systems Laboratory Located in EB 106 EB 106 Policies Introduction sessions Lab instructor CPE 323: Introduction to Embedded Computer Systems 2 CPE 323 Administration LAB Session on-line LAB manuals and tutorials Access cards Accounts Lab Assistant: Zahra Atashi Lab sessions (select 4 from the following list) Monday 8:00 - 9:30 AM Wednesday 8:00 - 9:30 AM Wednesday 5:30 - 7:00 PM Friday 8:00 - 9:30 AM Friday 9:30 – 11:00 AM Sign-up sheet will be available in the laboratory CPE 323: Introduction to Embedded Computer Systems 3 Outline Computer Engineering: Past, Present, Future Embedded systems What are they? Where do we find them? Structure and Organization Software Architectures CPE 323: Introduction to Embedded Computer Systems 4 What Is Computer Engineering? The creative application of engineering principles and methods to the design and development of hardware and software systems Discipline that combines elements of both electrical engineering and computer science Computer engineers are electrical engineers that have additional training in the areas of software design and hardware-software integration CPE 323: Introduction to Embedded Computer Systems 5 What Do Computer Engineers Do? Computer engineers are involved in all aspects of computing Design of computing devices (both Hardware and Software) Where are computing devices? Embedded computer systems (low-end – high-end) In: cars, aircrafts, home appliances, missiles, medical devices,..