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Lecture

Integers Representation: Sign vs. Two's Complement

In course

EE-110: Logic systems (for MT)

Ce cours couvre les fondements des systèmes numériques. Sur la base d'algèbre Booléenne et de circuitscombinatoires et séquentiels incluant les machines d'états finis, les methodes d'analyse et de synthèse de systèmelogiques sont étudiées et appliquée ...

Description

This lecture covers the representation of integers using sign-and-magnitude, one's-complement, and two's-complement notations. It explains how addition and subtraction are performed in these systems, highlighting the advantages of two's complement over sign-and-magnitude. The lecture also introduces the concept of excess-N representation and discusses overflow and underflow scenarios. Additionally, it delves into multiplication and division methods for integers, comparing binary division with the classic pen-and-paper method. Finally, it explores fixed-point and floating-point representations of real numbers, emphasizing the IEEE 754 standard for floating-point numbers.

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Instructor

Alexandre Schmid

Alexandre Schmid received the M.Sc. degree in microengineering and the Ph.D. degree in electrical engineering from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in 1994 and 2000, respectively. Since 1994, he has been with the EPFL, working with the Integrated Systems Laboratory as a Research and Teaching Assistant, and with the Electronics Laboratories as a Postdoctoral Fellow. In 2002, he was a Senior Research Associate with the Microelectronic Systems Laboratory, where he has been conducting research in the fields of bioelectronic interfaces and implantable biomedical electronics, nonconventional signal processing and neuromorphic hardware, and reliability of nanoelectronic devices, and also teaches with the Microengineering and Electrical Engineering Departments of EPFL. Since 2011, he is a Maître d'Enseignement et de Recherche (MER) Faculty Member with EPFL. He is a coauthor of two books, Reliability of Nanoscale Circuits and Systems, Methodologies and Circuit Architectures, Springer, 2011, and Wireless Cortical Implantable Systems, Springer, 2013, and a coeditor of one book, as well as over 100 articles published in journals and conferences. Dr. Schmid has served as the General Chair of the Fourth International Conference on Nano-Networks in 2009 and has been serving as an Associate Editor of the Institute of Electrical, Information, and Communication Engineers Electronics Express since 2009.

Official source

Ontological neighbourhood

Mathematics

Analysis: Numerical analysis

Arithmetic: Topics in arithmetic

Related concepts (32)

Floating-point arithmetic

In computing, floating-point arithmetic (FP) is arithmetic that represents subsets of real numbers using an integer with a fixed precision, called the significand, scaled by an integer exponent of a fixed base. Numbers of this form are called floating-point numbers. For example, 12.345 is a floating-point number in base ten with five digits of precision: However, unlike 12.345, 12.3456 is not a floating-point number in base ten with five digits of precision—it needs six digits of precision; the nearest floating-point number with only five digits is 12.

Double-precision floating-point format

Double-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point. Floating point is used to represent fractional values, or when a wider range is needed than is provided by fixed point (of the same bit width), even if at the cost of precision. Double precision may be chosen when the range or precision of single precision would be insufficient.

Single-precision floating-point format

Single-precision floating-point format (sometimes called FP32 or float32) is a computer number format, usually occupying 32 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point. A floating-point variable can represent a wider range of numbers than a fixed-point variable of the same bit width at the cost of precision. A signed 32-bit integer variable has a maximum value of 231 − 1 = 2,147,483,647, whereas an IEEE 754 32-bit base-2 floating-point variable has a maximum value of (2 − 2−23) × 2127 ≈ 3.

Integer overflow

In computer programming, an integer overflow occurs when an arithmetic operation attempts to create a numeric value that is outside of the range that can be represented with a given number of digits – either higher than the maximum or lower than the minimum representable value. The most common result of an overflow is that the least significant representable digits of the result are stored; the result is said to wrap around the maximum (i.e. modulo a power of the radix, usually two in modern computers, but sometimes ten or another radix).

Binary number

A binary number is a number expressed in the base-2 numeral system or binary numeral system, a method of mathematical expression which uses only two symbols: typically "0" (zero) and "1" (one). The base-2 numeral system is a positional notation with a radix of 2. Each digit is referred to as a bit, or binary digit. Because of its straightforward implementation in digital electronic circuitry using logic gates, the binary system is used by almost all modern computers and computer-based devices, as a preferred system of use, over various other human techniques of communication, because of the simplicity of the language and the noise immunity in physical implementation.

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