CBNST PPT, Floating point arithmetic,Normalization
•Download as PPT, PDF•
1 like•3,198 views
Representation of floating point arithmetic, Operation, Normalization, PIt Falls of floating point arithmetic
Report
Share
Report
Share
Recommended
I. AO* SEARCH ALGORITHM
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Unit 4-booth algorithm
Booth's algorithm is a method for multiplying two signed or unsigned integers in binary representation more efficiently than straightforward algorithms. It uses fewer additions and subtractions by representing the multiplicand as 2's complement numbers. The algorithm loads the multiplicand and multiplier into registers, initializes a third register to 0, and performs bitwise shifts and arithmetic operations (addition/subtraction of the multiplicand) on the registers based on the values of bits from the multiplier. This process builds up the product one bit at a time in a third register.
Data Structures- Part5 recursion
The document discusses recursion, including:
1) Recursion involves breaking a problem down into smaller subproblems until a base case is reached, then building up the solution to the overall problem from the solutions to the subproblems.
2) A recursive function is one that calls itself, with each call typically moving closer to a base case where the problem can be solved without recursion.
3) Recursion can be linear, involving one recursive call, or binary, involving two recursive calls to solve similar subproblems.
Assembly Language
Assembly language is a low-level programming language that corresponds directly to a processor's machine language instructions. It uses symbolic codes that are assembled into machine-readable object code. Assembly languages are commonly used when speed, compact code size, or direct hardware interaction is important. Assemblers translate assembly language into binary machine code that can be directly executed by processors.
Floating point representation
Real numbers can be stored using floating point representation, which separates a real number into three parts: a sign bit, exponent, and mantissa. The exponent indicates the power of the base 10 that the mantissa is multiplied by. Common standards like IEEE 754 define single and double precision formats that allocate more bits for higher precision at the cost of range. Summarizing a floating point number involves determining the exponent by shifting the decimal, converting the number to a leading digit mantissa, and writing the sign, exponent, and mantissa based on the specified precision format.
Graph traversals in Data Structures
Graph traversal techniques are used to search vertices in a graph and determine the order to visit vertices. There are two main techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue and visits the nearest vertices first, producing a spanning tree. DFS uses a stack and visits vertices by going as deep as possible first, also producing a spanning tree. Both techniques involve marking visited vertices to avoid loops.
AI3391 Artificial intelligence Unit IV Notes _ merged.pdf
This document provides an overview of first-order logic for knowledge representation in artificial intelligence. It discusses the syntax and semantics of first-order logic, including predicates, quantifiers, and variables. It also describes the knowledge engineering process for developing a first-order logic knowledge base, including identifying the problem domain, encoding general domain knowledge as rules, and representing a specific problem instance. Queries can then be posed to the knowledge base to infer answers using logical reasoning techniques like forward chaining and backward chaining.
Microoperations
This presentation discusses different types of microoperations that can be performed on data stored in registers. It describes arithmetic microoperations like addition, subtraction, and increment/decrement. Logic microoperations perform bit-wise operations on registers like selective set, clear, complement, and masking. Shift microoperations serially transfer data in a register left or right through logical, circular, and arithmetic shifts. Arithmetic shifts preserve a number's sign during multiplication and division by 2 during left and right shifts.
Recommended
I. AO* SEARCH ALGORITHM
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Unit 4-booth algorithm
Booth's algorithm is a method for multiplying two signed or unsigned integers in binary representation more efficiently than straightforward algorithms. It uses fewer additions and subtractions by representing the multiplicand as 2's complement numbers. The algorithm loads the multiplicand and multiplier into registers, initializes a third register to 0, and performs bitwise shifts and arithmetic operations (addition/subtraction of the multiplicand) on the registers based on the values of bits from the multiplier. This process builds up the product one bit at a time in a third register.
Data Structures- Part5 recursion
The document discusses recursion, including:
1) Recursion involves breaking a problem down into smaller subproblems until a base case is reached, then building up the solution to the overall problem from the solutions to the subproblems.
2) A recursive function is one that calls itself, with each call typically moving closer to a base case where the problem can be solved without recursion.
3) Recursion can be linear, involving one recursive call, or binary, involving two recursive calls to solve similar subproblems.
Assembly Language
Assembly language is a low-level programming language that corresponds directly to a processor's machine language instructions. It uses symbolic codes that are assembled into machine-readable object code. Assembly languages are commonly used when speed, compact code size, or direct hardware interaction is important. Assemblers translate assembly language into binary machine code that can be directly executed by processors.
Floating point representation
Real numbers can be stored using floating point representation, which separates a real number into three parts: a sign bit, exponent, and mantissa. The exponent indicates the power of the base 10 that the mantissa is multiplied by. Common standards like IEEE 754 define single and double precision formats that allocate more bits for higher precision at the cost of range. Summarizing a floating point number involves determining the exponent by shifting the decimal, converting the number to a leading digit mantissa, and writing the sign, exponent, and mantissa based on the specified precision format.
Graph traversals in Data Structures
Graph traversal techniques are used to search vertices in a graph and determine the order to visit vertices. There are two main techniques: breadth-first search (BFS) and depth-first search (DFS). BFS uses a queue and visits the nearest vertices first, producing a spanning tree. DFS uses a stack and visits vertices by going as deep as possible first, also producing a spanning tree. Both techniques involve marking visited vertices to avoid loops.
AI3391 Artificial intelligence Unit IV Notes _ merged.pdf
This document provides an overview of first-order logic for knowledge representation in artificial intelligence. It discusses the syntax and semantics of first-order logic, including predicates, quantifiers, and variables. It also describes the knowledge engineering process for developing a first-order logic knowledge base, including identifying the problem domain, encoding general domain knowledge as rules, and representing a specific problem instance. Queries can then be posed to the knowledge base to infer answers using logical reasoning techniques like forward chaining and backward chaining.
Microoperations
This presentation discusses different types of microoperations that can be performed on data stored in registers. It describes arithmetic microoperations like addition, subtraction, and increment/decrement. Logic microoperations perform bit-wise operations on registers like selective set, clear, complement, and masking. Shift microoperations serially transfer data in a register left or right through logical, circular, and arithmetic shifts. Arithmetic shifts preserve a number's sign during multiplication and division by 2 during left and right shifts.
Data Structure and Algorithms Binary Search Tree
Binary search trees (BSTs) are data structures that allow for efficient searching, insertion, and deletion. Nodes in a BST are organized so that all left descendants of a node are less than the node's value and all right descendants are greater. This property allows values to be found, inserted, or deleted in O(log n) time on average. Searching involves recursively checking if the target value is less than or greater than the current node's value. Insertion follows the search process and adds the new node in the appropriate place. Deletion handles three cases: removing a leaf, node with one child, or node with two children.
Principal source of optimization in compiler design
Unit 5 Principal source of optimization in compiler design in SRM IST, Kattankulathur by Prof. R. Rajkumar AP | CSE | SRM IST
Polygon clipping
The document discusses different algorithms for clipping polygons and lines to a viewing window, including the Sutherland-Hodgman and Weiler-Atherton polygon clipping algorithms. The Sutherland-Hodgman algorithm clips polygons by processing edges against each window boundary edge but can result in disconnected line segments or extraneous lines for concave polygons. The Weiler-Atherton algorithm addresses this by following either the polygon or window boundary depending on if the vertex pair is outside to inside or vice versa.
Syntax-Directed Translation into Three Address Code
The document discusses syntax-directed translation of code into three-address code. It defines semantic rules for generating three-address code for expressions, boolean expressions, and control flow statements. Temporary variables are generated for subexpressions and intermediate values. The semantic rules specify generating three-address code statements using temporary variables. Backpatching is also discussed as a technique to replace symbolic names in goto statements with actual addresses after code generation.
Computer graphics lab manual
The document provides a lab manual for computer graphics experiments in C language. It includes experiments on digital differential analyzer algorithm, Bresenham's line drawing algorithm, midpoint circle generation algorithm, ellipse generation algorithm, text and shape creation, 2D and 3D transformations, curve generation, and basic animations. It outlines the hardware and software requirements to run the experiments and provides background, algorithms, sample programs and outputs for each experiment.
Computer instructions
An instruction format consists of bits that specify an operation to perform on data in computer memory. The processor fetches instructions from memory and decodes the bits to execute them. Instruction formats have operation codes to define operations like addition and an address field to specify where data is located. Computers may have different instruction sets.
Control Units : Microprogrammed and Hardwired:control unit
The document discusses control units in CPUs. There are two main methods for implementing control units: hardwired and microprogrammed. A hardwired control unit generates control signals through circuitry using logic gates, while a microprogrammed control unit generates control signals by executing a stored microprogram. Overall, hardwired control units are faster but less flexible, while microprogrammed control units are slower but more flexible and modifiable.
COMPUTER ORGANIZATION NOTES Unit 2
This document discusses machine instructions and how programs are executed at the machine level. It covers number systems, data representation, memory addressing, instruction types, instruction execution, and addressing modes. Binary numbers are used in computers and represented as vectors. Negative numbers can be represented using sign-and-magnitude, one's complement, or two's complement methods. Memory is made up of addresses that store bits, bytes, and words of data. Instructions perform operations like data transfer, arithmetic, and program flow control. Programs are executed through sequential instruction fetch and execution, using techniques like looping and conditional branching. Addressing modes specify how operands are accessed in instructions.
Fill area algorithms
The document describes different algorithms for filling polygon and area shapes, including scanline fill, boundary fill, and flood fill algorithms. The scanline fill algorithm works by determining intersections of boundaries with scanlines and filling color between intersections. Boundary fill works by starting from an interior point and recursively "painting" neighboring points until the boundary is reached. Flood fill replaces a specified interior color. Both can be 4-connected or 8-connected. The document also discusses problems that can occur and more efficient span-based approaches.
Datapath Design of Computer Architecture
A datapath is a collection of functional units like ALUs and registers that perform data processing along with a control unit to form the CPU. There are three general steps to datapath design: 1) determine instruction classes, 2) design components for each class, and 3) combine the components. Common datapaths include load/store which uses memory addressing and branch/jump which uses instruction addressing. The ALU performs operations like addition and subtraction. The main control unit identifies instruction fields and controls the datapath. Multiplication can be done with combinational or sequential circuits while division similarly uses subtraction and shifting. Floating point uses separate exponent and mantissa fields.
Unit 3 dsa LINKED LIST
The document discusses linked lists, which are a linear data structure consisting of nodes connected to each other via pointers. Each node contains data and a pointer to the next node. There are several types of linked lists including singly linked lists where each node has a next pointer, doubly linked lists where each node has next and previous pointers, and circular linked lists where the last node points to the first node. The document covers terminology, advantages and disadvantages, operations, and implementations of different types of linked lists such as dynamic vs static memory allocation and uses in applications.
[1] Data Representation
The document discusses how computers represent data using binary numbers (1s and 0s). It explains that binary is used because it provides an easy way to represent two states (on/off) in storage devices. It then discusses how different numbers of bits (binary digits) can be used to represent different numbers in binary, and provides examples of converting between binary and decimal numbers. Finally, it briefly introduces the concept of data compression for reducing the size of files.
Associative memory 14208
Associative memory, also known as content-addressable memory (CAM), allows data to be searched based on its content rather than its location. It consists of a memory array, argument register (containing the search word), key register (specifying which bits to compare), and match register (indicating matching locations). All comparisons are done in parallel. Associative memory provides faster searching than conventional memory but is more expensive due to the additional comparison circuitry in each cell. It is well-suited for applications requiring very fast searching such as databases and virtual memory address translation.
Computer registers
A register is a group of flip-flops that can each store one bit of information. A processor uses registers to hold instructions, addresses, and data for manipulating information. The document lists several common computer registers - the Data Register stores 16-bit operands from memory, the Address Register holds 12-bit memory addresses, the Accumulator is a general purpose 16-bit processing register, and the Program Counter contains the 12-bit address of the next instruction. Temporary and input/output registers are also used to store intermediate data and user input/output respectively.
Access to non local names
This document discusses different approaches to implementing scope rules in programming languages. It begins by defining lexical/static scope and dynamic scope. It then discusses how block structure and nested procedures can be implemented using stacks and access links. Specifically, it describes how storage is allocated for local and non-local variables under lexical and dynamic scope models. The key implementation techniques discussed are stacks, access links, displays, deep access, and shallow access.
Indexing Data Structure
The document discusses R-trees, a data structure used to index multi-dimensional spatial data. R-trees allow for efficient searching of spatial data by grouping data into minimum bounding rectangles (MBRs) and storing them in a tree structure based on these envelopes. The tree structure resembles a B+-tree, with internal nodes containing pointers to child nodes or data records. R-trees provide efficient search, insertion, and deletion of spatial data objects through operations on the tree structure and splitting or merging of nodes as needed.
Randomized algorithms ver 1.0
This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
Semantic Networks
Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
Arithmetic micro operations
This document discusses various types of micro operations that can be performed at the digital component level in digital systems. It describes arithmetic micro operations like addition, subtraction, increment, decrement, and shift. It provides examples of how these operations are represented and implemented using registers and binary adders or subtractors. It also discusses logic micro operations and shift micro operations, providing examples of each type.
COMPUTER GRAPHICS-"Projection"
Projection is the transformation of a 3D object into a 2D plane by mapping points from the 3D object to the projection plane. There are two main types of projection: perspective projection and parallel projection. Perspective projection uses lines that converge to a single point, while parallel projection uses parallel lines. Perspective projection includes one-point, two-point, and three-point perspectives. Parallel projection includes orthographic projection, which projects lines perpendicular to the plane, and oblique projection, where lines are parallel but not perpendicular to the plane.
Floating Point Representation premium.pptx
This document discusses floating point representation of numbers in computers. It explains that there are two types of computer arithmetic: integer arithmetic and real arithmetic. Real arithmetic uses numbers with fractional parts and includes fixed point arithmetic and floating point arithmetic. Fixed point arithmetic represents numbers in binary form with a sign bit, integral part, and fractional part. Floating point representation uses scientific notation and normalized notation to represent numbers with a sign bit, mantissa, and exponent. It allows for a much larger range of numbers than fixed point representation.
Number system computer fundamental
This document discusses different number systems used in computers such as binary, decimal, octal and hexadecimal. It provides examples of converting between these number systems. The key points are:
- Computers use the binary number system and understand numbers as sequences of 0s and 1s.
- Other common number systems include decimal, octal and hexadecimal, which use different bases.
- Methods for converting between number systems include dividing the number by the new base or using shortcuts that group digits in specific ways.
More Related Content
What's hot
Data Structure and Algorithms Binary Search Tree
Binary search trees (BSTs) are data structures that allow for efficient searching, insertion, and deletion. Nodes in a BST are organized so that all left descendants of a node are less than the node's value and all right descendants are greater. This property allows values to be found, inserted, or deleted in O(log n) time on average. Searching involves recursively checking if the target value is less than or greater than the current node's value. Insertion follows the search process and adds the new node in the appropriate place. Deletion handles three cases: removing a leaf, node with one child, or node with two children.
Principal source of optimization in compiler design
Unit 5 Principal source of optimization in compiler design in SRM IST, Kattankulathur by Prof. R. Rajkumar AP | CSE | SRM IST
Polygon clipping
The document discusses different algorithms for clipping polygons and lines to a viewing window, including the Sutherland-Hodgman and Weiler-Atherton polygon clipping algorithms. The Sutherland-Hodgman algorithm clips polygons by processing edges against each window boundary edge but can result in disconnected line segments or extraneous lines for concave polygons. The Weiler-Atherton algorithm addresses this by following either the polygon or window boundary depending on if the vertex pair is outside to inside or vice versa.
Syntax-Directed Translation into Three Address Code
The document discusses syntax-directed translation of code into three-address code. It defines semantic rules for generating three-address code for expressions, boolean expressions, and control flow statements. Temporary variables are generated for subexpressions and intermediate values. The semantic rules specify generating three-address code statements using temporary variables. Backpatching is also discussed as a technique to replace symbolic names in goto statements with actual addresses after code generation.
Computer graphics lab manual
The document provides a lab manual for computer graphics experiments in C language. It includes experiments on digital differential analyzer algorithm, Bresenham's line drawing algorithm, midpoint circle generation algorithm, ellipse generation algorithm, text and shape creation, 2D and 3D transformations, curve generation, and basic animations. It outlines the hardware and software requirements to run the experiments and provides background, algorithms, sample programs and outputs for each experiment.
Computer instructions
An instruction format consists of bits that specify an operation to perform on data in computer memory. The processor fetches instructions from memory and decodes the bits to execute them. Instruction formats have operation codes to define operations like addition and an address field to specify where data is located. Computers may have different instruction sets.
Control Units : Microprogrammed and Hardwired:control unit
The document discusses control units in CPUs. There are two main methods for implementing control units: hardwired and microprogrammed. A hardwired control unit generates control signals through circuitry using logic gates, while a microprogrammed control unit generates control signals by executing a stored microprogram. Overall, hardwired control units are faster but less flexible, while microprogrammed control units are slower but more flexible and modifiable.
COMPUTER ORGANIZATION NOTES Unit 2
This document discusses machine instructions and how programs are executed at the machine level. It covers number systems, data representation, memory addressing, instruction types, instruction execution, and addressing modes. Binary numbers are used in computers and represented as vectors. Negative numbers can be represented using sign-and-magnitude, one's complement, or two's complement methods. Memory is made up of addresses that store bits, bytes, and words of data. Instructions perform operations like data transfer, arithmetic, and program flow control. Programs are executed through sequential instruction fetch and execution, using techniques like looping and conditional branching. Addressing modes specify how operands are accessed in instructions.
Fill area algorithms
The document describes different algorithms for filling polygon and area shapes, including scanline fill, boundary fill, and flood fill algorithms. The scanline fill algorithm works by determining intersections of boundaries with scanlines and filling color between intersections. Boundary fill works by starting from an interior point and recursively "painting" neighboring points until the boundary is reached. Flood fill replaces a specified interior color. Both can be 4-connected or 8-connected. The document also discusses problems that can occur and more efficient span-based approaches.
Datapath Design of Computer Architecture
A datapath is a collection of functional units like ALUs and registers that perform data processing along with a control unit to form the CPU. There are three general steps to datapath design: 1) determine instruction classes, 2) design components for each class, and 3) combine the components. Common datapaths include load/store which uses memory addressing and branch/jump which uses instruction addressing. The ALU performs operations like addition and subtraction. The main control unit identifies instruction fields and controls the datapath. Multiplication can be done with combinational or sequential circuits while division similarly uses subtraction and shifting. Floating point uses separate exponent and mantissa fields.
Unit 3 dsa LINKED LIST
The document discusses linked lists, which are a linear data structure consisting of nodes connected to each other via pointers. Each node contains data and a pointer to the next node. There are several types of linked lists including singly linked lists where each node has a next pointer, doubly linked lists where each node has next and previous pointers, and circular linked lists where the last node points to the first node. The document covers terminology, advantages and disadvantages, operations, and implementations of different types of linked lists such as dynamic vs static memory allocation and uses in applications.
[1] Data Representation
The document discusses how computers represent data using binary numbers (1s and 0s). It explains that binary is used because it provides an easy way to represent two states (on/off) in storage devices. It then discusses how different numbers of bits (binary digits) can be used to represent different numbers in binary, and provides examples of converting between binary and decimal numbers. Finally, it briefly introduces the concept of data compression for reducing the size of files.
Associative memory 14208
Associative memory, also known as content-addressable memory (CAM), allows data to be searched based on its content rather than its location. It consists of a memory array, argument register (containing the search word), key register (specifying which bits to compare), and match register (indicating matching locations). All comparisons are done in parallel. Associative memory provides faster searching than conventional memory but is more expensive due to the additional comparison circuitry in each cell. It is well-suited for applications requiring very fast searching such as databases and virtual memory address translation.
Computer registers
A register is a group of flip-flops that can each store one bit of information. A processor uses registers to hold instructions, addresses, and data for manipulating information. The document lists several common computer registers - the Data Register stores 16-bit operands from memory, the Address Register holds 12-bit memory addresses, the Accumulator is a general purpose 16-bit processing register, and the Program Counter contains the 12-bit address of the next instruction. Temporary and input/output registers are also used to store intermediate data and user input/output respectively.
Access to non local names
This document discusses different approaches to implementing scope rules in programming languages. It begins by defining lexical/static scope and dynamic scope. It then discusses how block structure and nested procedures can be implemented using stacks and access links. Specifically, it describes how storage is allocated for local and non-local variables under lexical and dynamic scope models. The key implementation techniques discussed are stacks, access links, displays, deep access, and shallow access.
Indexing Data Structure
The document discusses R-trees, a data structure used to index multi-dimensional spatial data. R-trees allow for efficient searching of spatial data by grouping data into minimum bounding rectangles (MBRs) and storing them in a tree structure based on these envelopes. The tree structure resembles a B+-tree, with internal nodes containing pointers to child nodes or data records. R-trees provide efficient search, insertion, and deletion of spatial data objects through operations on the tree structure and splitting or merging of nodes as needed.
Randomized algorithms ver 1.0
This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
Semantic Networks
Semantic networks are a knowledge representation technique where concepts are represented as nodes in a graph, and relationships between concepts are represented as links between nodes. There are different types of semantic networks, including definitional networks that emphasize subclass relationships, assertional networks for making propositions, and executable networks that can change based on operations. Common semantic relations include IS-A for subclasses, INSTANCE for examples, and HAS-PART for components. While semantic networks provide a natural representation of relationships, they have disadvantages like lack of standard link names and difficulty representing some logical constructs.
Arithmetic micro operations
This document discusses various types of micro operations that can be performed at the digital component level in digital systems. It describes arithmetic micro operations like addition, subtraction, increment, decrement, and shift. It provides examples of how these operations are represented and implemented using registers and binary adders or subtractors. It also discusses logic micro operations and shift micro operations, providing examples of each type.
COMPUTER GRAPHICS-"Projection"
Projection is the transformation of a 3D object into a 2D plane by mapping points from the 3D object to the projection plane. There are two main types of projection: perspective projection and parallel projection. Perspective projection uses lines that converge to a single point, while parallel projection uses parallel lines. Perspective projection includes one-point, two-point, and three-point perspectives. Parallel projection includes orthographic projection, which projects lines perpendicular to the plane, and oblique projection, where lines are parallel but not perpendicular to the plane.
What's hot (20)
Principal source of optimization in compiler design
Principal source of optimization in compiler design
Syntax-Directed Translation into Three Address Code
Syntax-Directed Translation into Three Address Code
Control Units : Microprogrammed and Hardwired:control unit
Control Units : Microprogrammed and Hardwired:control unit
Similar to CBNST PPT, Floating point arithmetic,Normalization
Floating Point Representation premium.pptx
This document discusses floating point representation of numbers in computers. It explains that there are two types of computer arithmetic: integer arithmetic and real arithmetic. Real arithmetic uses numbers with fractional parts and includes fixed point arithmetic and floating point arithmetic. Fixed point arithmetic represents numbers in binary form with a sign bit, integral part, and fractional part. Floating point representation uses scientific notation and normalized notation to represent numbers with a sign bit, mantissa, and exponent. It allows for a much larger range of numbers than fixed point representation.
Number system computer fundamental
This document discusses different number systems used in computers such as binary, decimal, octal and hexadecimal. It provides examples of converting between these number systems. The key points are:
- Computers use the binary number system and understand numbers as sequences of 0s and 1s.
- Other common number systems include decimal, octal and hexadecimal, which use different bases.
- Methods for converting between number systems include dividing the number by the new base or using shortcuts that group digits in specific ways.
number system: Floating Point representation.ppt
This document discusses floating point numbers, representation, arithmetic, and numeric coprocessors. It describes how floating point numbers are represented in binary using the sign, exponent, and significand. Arithmetic on floating point numbers is approximate due to limited precision. Numeric coprocessors perform floating point operations in hardware for improved speed over software methods. Examples demonstrate using a coprocessor to implement the quadratic formula, read arrays from files, and find prime numbers.
09 Arithmetic
The document discusses computer arithmetic and floating point number representation. It covers:
1) The Arithmetic Logic Unit (ALU) performs calculations and can handle integers and floating point numbers using separate floating point units.
2) Integer numbers are represented using binary and two's complement allows for positive and negative numbers. Floating point numbers use a sign bit, significand, and exponent in normalized form to represent numbers with fractions.
3) Operations like addition, subtraction, multiplication, and division are performed on integers in the ALU and floating point numbers following standard algorithms while managing overflow and normalization.
Representation of Real Numbers
Real numbers include whole numbers, rational numbers like fractions and decimals, and irrational numbers like pi. They can be positive, negative or zero. In computing, real numbers are represented using floating point notation, which stores numbers as a mantissa and exponent. The mantissa holds the significant digits of the number, while the exponent tracks the decimal place. Increasing the bit size of the mantissa improves accuracy, while increasing the exponent size expands the representable range of numbers.
Floating point arithmetic operations (1)
This document discusses floating point arithmetic operations including:
- The components of a floating point number including the mantissa and exponent.
- Normalization of floating point numbers to have a leading nonzero digit in the mantissa.
- Common floating point operations like addition, subtraction, multiplication, and division and how they are performed.
- The IEEE 754 standard for representing floating point numbers.
- How floating point arithmetic is implemented in hardware including registers and adders used to process mantissas and exponents.
Chapter 3
The document summarizes arithmetic operations for computers including integer and floating point numbers. It discusses addition, subtraction, multiplication, and division for integers and floating point numbers. It also describes common representations for floating point numbers according to the IEEE 754 standard and arithmetic operations on floating point numbers including addition, subtraction, multiplication, and division. Hardware implementations for integer and floating point arithmetic are also briefly discussed.
BOOTH ALGO, DIVISION(RESTORING _ NON RESTORING) etc etc
The document discusses various aspects of central processing unit (CPU) architecture and arithmetic operations. It covers the main components of a CPU - the arithmetic logic unit (ALU), control unit, and registers. It then describes different data representation methods including fixed-point and floating-point numbers. Various arithmetic operations for both types of numbers such as addition, subtraction, multiplication, and division are explained. Different adder designs like ripple-carry adder and carry lookahead adder are also summarized.
Integer Representation
The document discusses different methods for representing integers and fractional numbers in binary, including sign and modulus representation, one's complement, two's complement, fixed point representation, and floating point representation. It provides examples and activities to help understand how to convert between decimal and binary representations using these methods.
Counit2
1. The document describes the von Neumann architecture and its key components including the ALU, control unit, memory and I/O devices.
2. It explains the structure of the von Neumann machine and details the functions of components like the program counter, memory address register, and instruction register.
3. The document covers integer and floating point representation in binary, including sign-magnitude, two's complement, and IEEE 754 standard. It describes arithmetic operations like addition, subtraction, multiplication and division on binary numbers.
Number system in Digital Electronics
Contents:
1.What is number system?
2.Conversions of number from one radix to another
3.Complements (1's, 2's, 9's, 10's)
4.Binary Arithmetic ( Addition, subtraction, multiplication, division)
Computer Representation of Numbers and.pptx
- Computers use binary to represent numbers, where each digit is either a 1 or 0. Real numbers are approximated using floating point representation with sign, mantissa, and exponent fields.
- Integers can be stored by reserving bits for the magnitude and using the first bit to indicate sign (sign-magnitude representation) or by using two's complement representation where the most significant bit indicates sign.
- When storing numbers in memory, multiple bytes are typically used to represent integers or floating point values to support a wider range of numbers.
uyuyuy.pdf
This document discusses different number systems including decimal, binary, octal, and hexadecimal. It explains how to represent numbers in these different bases and how to convert between bases. Key points covered include binary arithmetic operations like addition, subtraction, multiplication, and division. Complement representations for negative numbers like 1's complement and 2's complement are also summarized.
SD & D Real Numbers
Real numbers include integers, rational numbers like fractions, and irrational numbers like pi. They can be positive, negative, or zero. In computing, real numbers are represented using floating point notation, where the number is stored as a mantissa and exponent. The mantissa holds the significant digits of the number, while the exponent tracks the decimal place. Increasing the bit sizes of the mantissa and exponent allows for greater accuracy or range of real numbers that can be represented, respectively.
IEEE Floating Point
The IEEE 754 standard defines the floating point representation of real numbers. It uses a sign-magnitude format that includes:
1) A sign bit to indicate positive or negative.
2) A biased exponent stored with an offset to allow negative exponents.
3) A mantissa or significant digits with a leading 1 not explicitly stored.
For single precision floats, this uses 32 bits broken into an 8-bit exponent and 23-bit mantissa. Doubles use 64 bits with an 11-bit exponent and 52-bit mantissa for greater precision and range.
A floating-point adder (IEEE 754 floating-point.pptx
This document describes the design and implementation of a 32-bit floating point adder according to the IEEE 754 standard using VHDL. It includes block diagrams of the main components: a pre-adder block to prepare the operands, an adder block to perform the addition or subtraction, and a standardization block to normalize the result. It also provides details on the steps involved, including extracting the sign, exponent and mantissa of the operands, handling special cases like zero, infinity and NaN, aligning the exponents, performing the addition or subtraction, normalizing and rounding the result, and adjusting the exponent.
Comp Arithmetic Basic.ppt
The document introduces computer architecture and system software. It discusses the differences between computer organization and computer architecture. It describes the basic components of a computer based on the Von Neumann architecture, which consists of four main sub-systems: memory, ALU, control unit, and I/O. The document also discusses bottlenecks of the Von Neumann architecture and differences between microprocessors and microcontrollers. It covers computer arithmetic concepts like integer representation, floating point representation using IEEE 754 standard, and number bases conversion. Additional topics include binary operations like addition, subtraction using complements, and multiplication algorithms like Booth's multiplication.
Chapter 6
The document discusses the Arithmetic Logic Unit (ALU) and how it handles integer and floating point arithmetic in a computer. The ALU performs calculations and is supported by other parts of the computer. It describes how integers are represented in binary using sign-magnitude and two's complement methods. Two's complement allows for easier arithmetic operations. Floating point numbers use a sign bit, exponent field, and significand to represent values with a fixed or floating decimal point. IEEE 754 standard defines common floating point formats. The ALU performs operations on operands from registers and stores results back in registers.
Chapter 02
- The document discusses counting and rounding significant figures when performing calculations with measurements. It provides rules for determining the number of significant figures in products, quotients, sums, and differences.
- It also discusses common units in the International System of Units (SI) including prefixes, and provides examples of unit conversions using dimensional analysis and setting up conversion factors.
Similar to CBNST PPT, Floating point arithmetic,Normalization (20)
BOOTH ALGO, DIVISION(RESTORING _ NON RESTORING) etc etc
BOOTH ALGO, DIVISION(RESTORING _ NON RESTORING) etc etc
A floating-point adder (IEEE 754 floating-point.pptx
A floating-point adder (IEEE 754 floating-point.pptx
Recently uploaded
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Adaptive synchronous sliding control for a robot manipulator based on neural ...
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单专业办理
UMich毕业证原版定制【微信：176555708】【密歇根大学|安娜堡分校毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
bank management system in java and mysql report1.pdf
truth is high but higher still is truth full living
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单专业办理
IIT毕业证原版定制【微信：176555708】【伊利诺伊理工大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
Technical Drawings introduction to drawing of prisms
Method of technical Drawing of prisms,and cylinders.
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
Kinematics 11th jpp- 01. ( Solved ) unacademy namo kaul on 14th may...
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Dynamic Programming
Backtracking
Techniques for Graphs
Branch and Bound
22CYT12-Unit-V-E Waste and its Management.ppt
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
PROJECT FORMAT FOR EVS AMITY UNIVERSITY GWALIOR.ppt
Ppt on evs amity University project work.........................??????!?!?!?!!?!!?!!?/?/?/?/?/?
Hierarchical Digital Twin of a Naval Power System
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样
学校原件一模一样【微信：741003700 】《(ANU毕业证书)澳洲国立大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Recently uploaded (20)
Generative AI leverages algorithms to create various forms of content
Generative AI leverages algorithms to create various forms of content
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
Adaptive synchronous sliding control for a robot manipulator based on neural ...
Adaptive synchronous sliding control for a robot manipulator based on neural ...
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
bank management system in java and mysql report1.pdf
bank management system in java and mysql report1.pdf
Technical Drawings introduction to drawing of prisms
Technical Drawings introduction to drawing of prisms
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
PROJECT FORMAT FOR EVS AMITY UNIVERSITY GWALIOR.ppt
PROJECT FORMAT FOR EVS AMITY UNIVERSITY GWALIOR.ppt
CBNST PPT, Floating point arithmetic,Normalization
- 1. Floating point arithmetic Representation of floating point arithmetic Operations Normalization Pit-Falls of floating point arithmetic
- 2. There are two types of arithmetic operations :- Integer Arithmetic Real – floating point arithmetic Integer Arithmetic :- deals with integer operands. i.e. - num without fractional parts Real Arithmetic :- use number with fractional parts as operands and is use Real no. = mantissa * 10^exponent
- 3. COMPUTER REPRESENTATION OF FLOATING POINT NUMBERS In the CPU, a 32-bit floating point number is represented using IEEE standard format as follows: S | EXPONENT | MANTISSA where S is one bit, the EXPONENT is 8 bits, and the MANTISSA is 23 bits.
- 4. The mantissa represents the leading significant bits in the number. It is made less than 1 and greater than or equal to 0.1 The exponent is used to adjust the position of the binary point (as opposed to a "decimal" point) In power of 10 and multiplies with mantissa
- 5. Normalization :- Mantissa and Exponent have their own independent signs While storing no. of the leading digit in the mantissa, mantissa is always made non- zero by appropriately shifting it and adjusting the value of the exponent The shifting of mantissa to the left till its most significant digit is non- zero is called normalization.
- 6. Arithmetic operations with normalized floating point numbers Addition Subtraction Multiplication Division
- 7. Addition :- Consider a 4-digit decimal example 9.999 × 101 + 1.610 × 10–1 1. Align decimal points Shift number with smaller exponent 9.999 × 101 + 0.016 × 101 2. Add significant 9.999 × 101 + 0.016 × 101 = 10.015 × 101 3. Normalize result & check for over/underflow 4. Round and renormalize if necessary
- 8. Subtraction :- Consider a 4-digit decimal example 9.999 × 101 - 1.610 × 10–1 1. Align decimal points Shift number with smaller exponent 9.999 × 101 - 0.016 × 101 2. Subtract significant 9.999 × 101 - 0.016 × 101 = 9.983 × 101 3. Normalize result & check for over/underflow 4. Round and renormalize if necessary
- 9. Multiplication :- Consider a 4-digit decimal example 1.110 × 1010 × 9.200 × 10–5 1. Add exponents New exponent = 10 + –5 = 5 2. Multiply significant 1.110 × 9.200 = 10.212 10.212 × 105 3. Normalize result & check for over/underflow 1.0212 × 106 4. Round and renormalize if necessary 1.021 × 106 5. Determine sign of result from signs of operands +1.021 × 106
- 10. Division :- Consider a 4-digit decimal example 1.110 × 1010 / 9.200 × 10–5 1. Subtract exponents New exponent = 10 – (–5) = 15 2. Divide significant 1.110 / 9.200 = 0.12065 0.12065 × 1015 3. Normalize result & check for over/underflow 1.2065 × 1014 4. Round and renormalize if necessary 5. Determine sign of result from signs of operands