THE DESIGN of an IC HALF PRECISION FLOATING POINT ARITHMETIC LOGIC UNIT Balaji Kannan Clemson University, [email protected]
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Type-Safe Composition of Object Modules*
International Conference on Computer Systems and Education I ISc Bangalore Typ esafe Comp osition of Ob ject Mo dules Guruduth Banavar Gary Lindstrom Douglas Orr Department of Computer Science University of Utah Salt LakeCity Utah USA Abstract Intro duction It is widely agreed that strong typing in We describ e a facility that enables routine creases the reliability and eciency of soft typ echecking during the linkage of exter ware However compilers for statically typ ed nal declarations and denitions of separately languages suchasC and C in tradi compiled programs in ANSI C The primary tional nonintegrated programming environ advantage of our serverstyle typ echecked ments guarantee complete typ esafety only linkage facility is the ability to program the within a compilation unit but not across comp osition of ob ject mo dules via a suite of suchunits Longstanding and widely avail strongly typ ed mo dule combination op era able linkers comp ose separately compiled tors Such programmability enables one to units bymatching symb ols purely byname easily incorp orate programmerdened data equivalence with no regard to their typ es format conversion stubs at linktime In ad Such common denominator linkers accom dition our linkage facility is able to automat mo date ob ject mo dules from various source ically generate safe co ercion stubs for com languages by simply ignoring the static se patible encapsulated data mantics of the language Moreover com monly used ob ject le formats are not de signed to incorp orate source language typ e -
Pragmatic Quotient Types in Coq Cyril Cohen
Pragmatic Quotient Types in Coq Cyril Cohen To cite this version: Cyril Cohen. Pragmatic Quotient Types in Coq. International Conference on Interactive Theorem Proving, Jul 2013, Rennes, France. pp.16. hal-01966714 HAL Id: hal-01966714 https://hal.inria.fr/hal-01966714 Submitted on 29 Dec 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Pragmatic Quotient Types in Coq Cyril Cohen Department of Computer Science and Engineering University of Gothenburg [email protected] Abstract. In intensional type theory, it is not always possible to form the quotient of a type by an equivalence relation. However, quotients are extremely useful when formalizing mathematics, especially in algebra. We provide a Coq library with a pragmatic approach in two complemen- tary components. First, we provide a framework to work with quotient types in an axiomatic manner. Second, we program construction mecha- nisms for some specific cases where it is possible to build a quotient type. This library was helpful in implementing the types of rational fractions, multivariate polynomials, field extensions and real algebraic numbers. Keywords: Quotient types, Formalization of mathematics, Coq Introduction In set-based mathematics, given some base set S and an equivalence ≡, one may see the quotient (S/ ≡) as the partition {π(x) | x ∈ S} of S into the sets π(x) ˆ= {y ∈ S | x ≡ y}, which are called equivalence classes. -
Nios II Custom Instruction User Guide
Nios II Custom Instruction User Guide Subscribe UG-20286 | 2020.04.27 Send Feedback Latest document on the web: PDF | HTML Contents Contents 1. Nios II Custom Instruction Overview..............................................................................4 1.1. Custom Instruction Implementation......................................................................... 4 1.1.1. Custom Instruction Hardware Implementation............................................... 5 1.1.2. Custom Instruction Software Implementation................................................ 6 2. Custom Instruction Hardware Interface......................................................................... 7 2.1. Custom Instruction Types....................................................................................... 7 2.1.1. Combinational Custom Instructions.............................................................. 8 2.1.2. Multicycle Custom Instructions...................................................................10 2.1.3. Extended Custom Instructions................................................................... 11 2.1.4. Internal Register File Custom Instructions................................................... 13 2.1.5. External Interface Custom Instructions....................................................... 15 3. Custom Instruction Software Interface.........................................................................16 3.1. Custom Instruction Software Examples................................................................... 16 -
Faster Ffts in Medium Precision
Faster FFTs in medium precision Joris van der Hoevena, Grégoire Lecerfb Laboratoire d’informatique, UMR 7161 CNRS Campus de l’École polytechnique 1, rue Honoré d’Estienne d’Orves Bâtiment Alan Turing, CS35003 91120 Palaiseau a. Email: [email protected] b. Email: [email protected] November 10, 2014 In this paper, we present new algorithms for the computation of fast Fourier transforms over complex numbers for “medium” precisions, typically in the range from 100 until 400 bits. On the one hand, such precisions are usually not supported by hardware. On the other hand, asymptotically fast algorithms for multiple precision arithmetic do not pay off yet. The main idea behind our algorithms is to develop efficient vectorial multiple precision fixed point arithmetic, capable of exploiting SIMD instructions in modern processors. Keywords: floating point arithmetic, quadruple precision, complexity bound, FFT, SIMD A.C.M. subject classification: G.1.0 Computer-arithmetic A.M.S. subject classification: 65Y04, 65T50, 68W30 1. Introduction Multiple precision arithmetic [4] is crucial in areas such as computer algebra and cryptography, and increasingly useful in mathematical physics and numerical analysis [2]. Early multiple preci- sion libraries appeared in the seventies [3], and nowadays GMP [11] and MPFR [8] are typically very efficient for large precisions of more than, say, 1000 bits. However, for precisions which are only a few times larger than the machine precision, these libraries suffer from a large overhead. For instance, the MPFR library for arbitrary precision and IEEE-style standardized floating point arithmetic is typically about a factor 100 slower than double precision machine arithmetic. -
Harnessing Numerical Flexibility for Deep Learning on Fpgas.Pdf
WHITE PAPER FPGA Inline Acceleration Harnessing Numerical Flexibility for Deep Learning on FPGAs Authors Abstract Andrew C . Ling Deep learning has become a key workload in the data center and the edge, leading [email protected] to a race for dominance in this space. FPGAs have shown they can compete by combining deterministic low latency with high throughput and flexibility. In Mohamed S . Abdelfattah particular, FPGAs bit-level programmability can efficiently implement arbitrary [email protected] precisions and numeric data types critical in the fast evolving field of deep learning. Andrew Bitar In this paper, we explore FPGA minifloat implementations (floating-point [email protected] representations with non-standard exponent and mantissa sizes), and show the use of a block-floating-point implementation that shares the exponent across David Han many numbers, reducing the logic required to perform floating-point operations. [email protected] The paper shows this technique can significantly improve FPGA performance with no impact to accuracy, reduce logic utilization by 3X, and memory bandwidth and Roberto Dicecco capacity required by more than 40%.† [email protected] Suchit Subhaschandra Introduction [email protected] Deep neural networks have proven to be a powerful means to solve some of the Chris N Johnson most difficult computer vision and natural language processing problems since [email protected] their successful introduction to the ImageNet competition in 2012 [14]. This has led to an explosion of workloads based on deep neural networks in the data center Dmitry Denisenko and the edge [2]. [email protected] One of the key challenges with deep neural networks is their inherent Josh Fender computational complexity, where many deep nets require billions of operations [email protected] to perform a single inference. -
Midterm-2020-Solution.Pdf
HONOR CODE Questions Sheet. A Lets C. [6 Points] 1. What type of address (heap,stack,static,code) does each value evaluate to Book1, Book1->name, Book1->author, &Book2? [4] 2. Will all of the print statements execute as expected? If NO, write print statement which will not execute as expected?[2] B. Mystery [8 Points] 3. When the above code executes, which line is modified? How many times? [2] 4. What is the value of register a6 at the end ? [2] 5. What is the value of register a4 at the end ? [2] 6. In one sentence what is this program calculating ? [2] C. C-to-RISC V Tree Search; Fill in the blanks below [12 points] D. RISCV - The MOD operation [8 points] 19. The data segment starts at address 0x10000000. What are the memory locations modified by this program and what are their values ? E Floating Point [8 points.] 20. What is the smallest nonzero positive value that can be represented? Write your answer as a numerical expression in the answer packet? [2] 21. Consider some positive normalized floating point number where p is represented as: What is the distance (i.e. the difference) between p and the next-largest number after p that can be represented? [2] 22. Now instead let p be a positive denormalized number described asp = 2y x 0.significand. What is the distance between p and the next largest number after p that can be represented? [2] 23. Sort the following minifloat numbers. [2] F. Numbers. [5] 24. What is the smallest number that this system can represent 6 digits (assume unsigned) ? [1] 25. -
The MPFR Team [email protected] This Manual Documents How to Install and Use the Multiple Precision Floating-Point Reliable Library, Version 2.2.0
MPFR The Multiple Precision Floating-Point Reliable Library Edition 2.2.0 September 2005 The MPFR team [email protected] This manual documents how to install and use the Multiple Precision Floating-Point Reliable Library, version 2.2.0. Copyright 1991, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with no Invariant Sections, with the Front-Cover Texts being “A GNU Manual”, and with the Back-Cover Texts being “You have freedom to copy and modify this GNU Manual, like GNU software”. A copy of the license is included in Appendix A [GNU Free Documentation License], page 30. i Table of Contents MPFR Copying Conditions ................................ 1 1 Introduction to MPFR ................................. 2 1.1 How to use this Manual ........................................................ 2 2 Installing MPFR ....................................... 3 2.1 How to install ................................................................. 3 2.2 Other make targets ............................................................ 3 2.3 Known Build Problems ........................................................ 3 2.4 Getting the Latest Version of MPFR ............................................ 4 3 Reporting Bugs ........................................ 5 4 MPFR Basics ......................................... -
IEEE Std 754-1985 Revision of Reaffirmed1990 IEEE Standard for Binary Floating-Point Arithmetic
Recognized as an American National Standard (ANSI) IEEE Std 754-1985 An American National Standard IEEE Standard for Binary Floating-Point Arithmetic Sponsor Standards Committee of the IEEE Computer Society Approved March 21, 1985 Reaffirmed December 6, 1990 IEEE Standards Board Approved July 26, 1985 Reaffirmed May 21, 1991 American National Standards Institute © Copyright 1985 by The Institute of Electrical and Electronics Engineers, Inc 345 East 47th Street, New York, NY 10017, USA No part of this publication may be reproduced in any form, in an electronic retrieval system or otherwise, without the prior written permission of the publisher. IEEE Standards documents are developed within the Technical Committees of the IEEE Societies and the Standards Coordinating Committees of the IEEE Standards Board. Members of the committees serve voluntarily and without compensation. They are not necessarily members of the Institute. The standards developed within IEEE represent a consensus of the broad expertise on the subject within the Institute as well as those activities outside of IEEE which have expressed an interest in participating in the development of the standard. Use of an IEEE Standard is wholly voluntary. The existence of an IEEE Standard does not imply that there are no other ways to produce, test, measure, purchase, market, or provide other goods and services related to the scope of the IEEE Standard. Furthermore, the viewpoint expressed at the time a standard is approved and issued is subject to change brought about through developments in the state of the art and comments received from users of the standard. Every IEEE Standard is subjected to review at least once every five years for revision or reaffirmation. -
GNU MPFR the Multiple Precision Floating-Point Reliable Library Edition 4.1.0 July 2020
GNU MPFR The Multiple Precision Floating-Point Reliable Library Edition 4.1.0 July 2020 The MPFR team [email protected] This manual documents how to install and use the Multiple Precision Floating-Point Reliable Library, version 4.1.0. Copyright 1991, 1993-2020 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, with no Front-Cover Texts, and with no Back- Cover Texts. A copy of the license is included in Appendix A [GNU Free Documentation License], page 59. i Table of Contents MPFR Copying Conditions ::::::::::::::::::::::::::::::::::::::: 1 1 Introduction to MPFR :::::::::::::::::::::::::::::::::::::::: 2 1.1 How to Use This Manual::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 2 2 Installing MPFR ::::::::::::::::::::::::::::::::::::::::::::::: 3 2.1 How to Install ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 3 2.2 Other `make' Targets :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 4 2.3 Build Problems :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 4 2.4 Getting the Latest Version of MPFR ::::::::::::::::::::::::::::::::::::::::::::::: 4 3 Reporting Bugs::::::::::::::::::::::::::::::::::::::::::::::::: 5 4 MPFR Basics ::::::::::::::::::::::::::::::::::::::::::::::::::: 6 4.1 Headers and Libraries :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 6 -
Arithmetic in MIPS
6 Arithmetic in MIPS Objectives After completing this lab you will: • know how to do integer arithmetic in MIPS • know how to do floating point arithmetic in MIPS • know about conversion from integer to floating point and from floating point to integer. Introduction The definition of the R2000 architecture includes all integer arithmetic within the actual CPU. Floating point arithmetic is done by one of the four possible coprocessors, namely coprocessor number 1. Integer arithmetic Addition and subtraction are performed on 32 bit numbers held in the general purpose registers (32 bit each). The result is a 32 bit number itself. Two kinds of instructions are included in the instruction set to do integer addition and subtraction: • instructions for signed arithmetic: the 32 bit numbers are considered to be represented in 2’s comple- ment. The execution of the instruction (addition or subtraction) may generate an overflow. • instructions for unsigned arithmetic: the 32 bit numbers are considered to be in standard binary repre- sentation. Executing the instruction will never generate an overflow error even if there is an actual overflow (the result cannot be represented with 32 bits). Multiplication and division may generate results that are larger than 32 bits. The architecture provides two special 32 bit registers that are the destination for multiplication and division instructions. These registers are called hi and lo as to indicate that they hold the higher 32 bits of the result and the lower 32 bits respectively. Special instructions are also provided to move data from these registers into the general purpose ones ($0 to $31). -
IEEE Standard 754 for Binary Floating-Point Arithmetic
Work in Progress: Lecture Notes on the Status of IEEE 754 October 1, 1997 3:36 am Lecture Notes on the Status of IEEE Standard 754 for Binary Floating-Point Arithmetic Prof. W. Kahan Elect. Eng. & Computer Science University of California Berkeley CA 94720-1776 Introduction: Twenty years ago anarchy threatened floating-point arithmetic. Over a dozen commercially significant arithmetics boasted diverse wordsizes, precisions, rounding procedures and over/underflow behaviors, and more were in the works. “Portable” software intended to reconcile that numerical diversity had become unbearably costly to develop. Thirteen years ago, when IEEE 754 became official, major microprocessor manufacturers had already adopted it despite the challenge it posed to implementors. With unprecedented altruism, hardware designers had risen to its challenge in the belief that they would ease and encourage a vast burgeoning of numerical software. They did succeed to a considerable extent. Anyway, rounding anomalies that preoccupied all of us in the 1970s afflict only CRAY X-MPs — J90s now. Now atrophy threatens features of IEEE 754 caught in a vicious circle: Those features lack support in programming languages and compilers, so those features are mishandled and/or practically unusable, so those features are little known and less in demand, and so those features lack support in programming languages and compilers. To help break that circle, those features are discussed in these notes under the following headings: Representable Numbers, Normal and Subnormal, Infinite -
Quotient Types: a Modular Approach
Quotient Types: A Modular Approach Aleksey Nogin Department of Computer Science Cornell University, Ithaca, NY 14853 [email protected] Abstract. In this paper we introduce a new approach to axiomatizing quotient types in type theory. We suggest replacing the existing mono- lithic rule set by a modular set of rules for a specially chosen set of primitive operations. This modular formalization of quotient types turns out to be much easier to use and free of many limitations of the tradi- tional monolithic formalization. To illustrate the advantages of the new approach, we show how the type of collections (that is known to be very hard to formalize using traditional quotient types) can be naturally for- malized using the new primitives. We also show how modularity allows us to reuse one of the new primitives to simplify and enhance the rules for the set types. 1 Introduction NuPRL type theory differs from most other type theories used in theorem provers in its treatment of equality. In Coq’s Calculus of Constructions, for example, there is a single global equality relation which is not the desired one for many types (e.g. function types). The desired equalities have to be handled explicitly, which is quite burdensome. As in Martin-L¨of type theory [20] (of which NuPRL type theory is an extension), in NuPRL each type comes with its own equality relation (the extensional one in the case of functions), and the typing rules guar- antee that well–typed terms respect these equalities. Semantically, a quotient in NuPRL is trivial to define: it is simply a type with a new equality.