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[course assignment] floating point operation analysis and precision improvement

2022-06-21 13:06:00 【51CTO】

Abstract
What is it?
disadvantages
Why?
How do you do it?
Convert decimals to fractions
Using arrays
C Function library settings
Math library
experiment
Python Fractional operation
Using arrays
C Function library settings
Math library
summary
references

https://baike.baidu.com/item/%E6%B5%AE%E7%82%B9%E8%BF%90%E7%AE%97%E5%99%A8

https://cn.bing.com/search?q=python+%E5%88%86%E6%95%B0%E8%BF%90%E7%AE%97&qs=n&form=QBRE&sp=-1&pq=python+%E5%88%86%E6%95%B0&sc=0-9&sk=&cvid=BDBA7952446B4CE98E970FBD1B1C570B

https://www.jianshu.com/p/6073b6ecc1e5

https://zhuanlan.zhihu.com/p/57010602

http://c.biancheng.net/view/314.html

List of articles

C Primer Plus

IEC Floating point standard
fenv.h The header file
STDC FP_CONTRACT Compilation instructions
The math library broke its heart for floating-point arithmetic

Computer system structure

Floating point table number range
Floating point table number precision

《 Design and implementation of fast single precision floating point arithmetic unit 》
《 be based on FPGA Research on floating point arithmetic unit 》

1. introduction
2. Floating point means
3. Research on normalized floating point number basic operation and hardware circuit

《 be based on RISC-V Floating point instruction set FPU Research and design of 》
《 Research on high precision algorithm based on multi part floating point representation 》

The history of floating point numbers
The definition of floating point numbers
== Problems with floating point numbers ==

《 be based on FPGA Research and implementation of single and double precision floating point arithmetic unit based on 》

1.2 Research status of floating point arithmetic units at home and abroad
2.1 Floating point format parsing
The third chapter Research on Algorithm of floating point arithmetic unit

3.1 Floating point addition and subtraction
3.2 Floating point multiplication
3.3 Floating point division

【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point

C Primer Plus

float、double、long double

float Is the basic floating point type in the system . It can at least accurately represent 6 Significant digits , Usually 32 position .
double May represent a larger floating point number . It may mean more than float More significant figures and bigger indices . It can at least accurately represent 10 Significant digits , Usually 64 position .
long double May represent a larger floating point number . It can express ratio double More significant figures and bigger indices .

Floating point constants

By default , The compiler assumes that floating-point constants are double Accuracy of type . hypothesis some yes float Variable of type ,some = 4.0 * 2.0; Usually ,4.0 and 2.0 Will be stored as 64 Bit double type , Multiplication with double precision , Then truncate the product to float The width of the type . This will improve the calculation accuracy , But it will slow down the running speed of the program .

Add... After the floating point number f or F Suffixes override the default settings , The compiler treats floating-point constants as float type , Use l or L Suffixes make numbers long double type .

Print floating point values

       
       jiaming
       
       @jiaming
       
       -
       
       System
       
       -
       
       Product
       
       -
       
       Name:
       
       /
       
       tmp$ .
       
       /
       
       float.
       
       o 
       
       32000.000000 
       
       can 
       
       be 
       
       weitten 
       
       3.200000e+04
       
       And 
       
       it
       
       's 0x1.f4p+14 in hexadecimal, powers of 2 notation
       
       2140000000.000000 
       
       can 
       
       be 
       
       written 
       
       2.140000e+09
       
       0.000053 
       
       can 
       
       be 
       
       written 
       
       5.320000e-05
       
       jiaming
       
       @jiaming
       
       -
       
       System
       
       -
       
       Product
       
       -
       
       Name:
       
       /
       
       tmp$ 
       
       cat 
       
       float.
       
       c
       
       /*************************************************************************
       
       > File Name: float.c
       
       > Author: jiaming
       
       > Mail: [email protected] 
       
       > Created Time: 2020 year 12 month 27 Japan   Sunday  16 when 33 branch 11 second 
       
       ************************************************************************/
       
       # include<stdio.h>
       
       int 
       
       main()
       
{
       
       float 
       
       aboat 
       
       = 
       
       32000.0;
       
       double 
       
       abet 
       
       = 
       
       2.14e9;
       
       long 
       
       double 
       
       dip 
       
       = 
       
       5.32e-5;
       
       printf(
       
       "%f can be weitten %e\n", 
       
       aboat, 
       
       aboat);
       
       // c99
       
       printf(
       
       "And it's %a in hexadecimal, powers of 2 notation\n", 
       
       aboat);
       
       printf(
       
       "%f can be written %e\n", 
       
       abet, 
       
       abet);
       
       printf(
       
       "%Lf can be written %Le\n", 
       
       dip, 
       
       dip);
       
       return 
       
       0;
       
}
      
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Overflow and underflow of floating-point values

       
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       tmp.
       
       o 
       
       inf
       
       jiaming
       
       @jiaming
       
       -
       
       System
       
       -
       
       Product
       
       -
       
       Name:
       
       /
       
       tmp$ 
       
       cat 
       
       tmp.
       
       c
       
       /*************************************************************************
       
       > File Name: tmp.c
       
       > Author: jiaming
       
       > Mail: [email protected] 
       
       > Created Time: 2020 year 12 month 27 Japan   Sunday  16 when 59 branch 35 second 
       
       ************************************************************************/
       
       # include<stdio.h>
       
       int 
       
       main()
       
{
       
       float 
       
       toobig 
       
       = 
       
       9.9E99;
       
       printf(
       
       "%f\n", 
       
       toobig);
       
       return 
       
       0;
       
}
      
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Floating point rounding error

       
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       tmp.
       
       o 
       
       4008175468544.000000
       
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       Product
       
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       Name:
       
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       tmp$ 
       
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       tmp.
       
       c
       
       /*************************************************************************
       
       > File Name: tmp.c
       
       > Author: jiaming
       
       > Mail: [email protected] 
       
       > Created Time: 2020 year 12 month 27 Japan   Sunday  16 when 59 branch 35 second 
       
       ************************************************************************/
       
       # include<stdio.h>
       
       int 
       
       main()
       
{
       
       float 
       
       a, 
       
       b;
       
       b 
       
       = 
       
       2.0e20 
       
       + 
       
       1.0;
       
       a 
       
       = 
       
       b 
       
       - 
       
       2.0e20;
       
       printf(
       
       "%f\n", 
       
       a);
       
       return 
       
       0;
       
}
      
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as a result of ： The computer lacks enough decimal places to complete the correct calculation .2.0e20 yes 2 In the back 20 individual 0. If you add this number 1, Then what has changed is 21 position . Calculate correctly , The program must store at least 21 Digit number , and float Types of numbers usually store only exponentially scaled down or enlarged numbers 6 or 7 Significant digits . under these circumstances , The calculation must be wrong . On the other hand , If you put 2.0e20 Change to 2.0e4, The calculation result is no problem . because 2.0e4 Add 1 Just change the second 5 A number in bits ,float The precision of the type is sufficient for such an operation .

IEC Floating point standard

Floating point model

International Electrotechnical Commission （IEC） Issued a set of standards for floating-point computing （IEC 60559）.

C99 Most of the new floating point tools （ Such as ,fenv.h Header files and some new math functions ） Are based on this , in addition ,float.h The header file defines some IEC Floating point model related macros .

【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _02

【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _03 Representative symbol （±1）
Represents the base number . The most common value is 2, Because floating-point processors usually use binary numbers .
Represents the integer exponent , Limit the minimum and maximum values . These values depend on the number of digits set aside to store the index .
【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _06 The representative cardinality is The possible number . for example , The base number is 2 when , The possible numbers are 0 and 1; In hexadecimal , The possible numbers are 0~F.
Represents precision , The base number is 【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _04 when , Represents the number of significant digits . Its value is limited by the number of digits reserved for storing significant digits .

give an example ：
24.51=(±1)10^3(2/10+4/100+5/1000+1/10000+0/100000)

Assume the sign is positive , base b by 2,p yes 7（ use 7 Bit binary number means ）, The index is 5, The valid number with storage is 1011001.
24.25=(±1)2^5(1/2+0/4+1/8+1/16+0/32+0/64+1/128)

Normal and below normal values

give an example ：
Decimal system b=10; Precision of floating point values p=5;

Index = 3, Effective number .31841(.31841E3)
Index = 4, Effective number .03184(.03184E4)
Index = 5, Effective number .00318(.00318E5)

The first 1 The two kinds of precision are the highest , Because we use all the in the valid numbers 5 Bits available bits . Normalizing floating-point nonzero values is the 1 The bit significand is a non-zero value , This is also the usual way to store floating-point numbers .

hypothesis , Minimum index （FLT_MIN_EXP） by -10, Then the minimum specification value is ：

Index = -10, Effective number = .10000(.10000E-10)

Usually , Multiply or divide by 10 It means that the index increases or decreases , But in this case , If divided by 10, But I can't reduce the index any more . however , This representation can be obtained by changing the significant number ：

Index = -10, Effective number = .01000(.01000E-10)

This number is called below normal , Because this number does not use the full precision of the significant number , Lost a bit of information .
For this assumption ,0.1000E-10 Is the minimum non-zero normal value （FLT_MIN）, The minimum nonzero below normal is 0.00001E-10（FLT_TRUE_MIN）.
float.h Macro in FLT_HAS_SUBNURM、DBL_HAS_SUBNORM、LDBL_HAS_SUBNORM Characterizes how the implementation handles below normal values . Here are the values that these macros may use and their meanings ：

-1： Not sure
0： non-existent （ May use 0 Replace lower than normal values ）
1： There is

math.h The library provides some methods , Include fpclassify() and isnormal() macro , It can identify when the program generates a lower than normal value , This will lose some accuracy .

Evaluation scheme

float.h Macro in FLT_EVAL_METHOD It determines the evaluation scheme of the floating-point expression used in the implementation ：

-1： Not sure
0： For operations within the range and precision of all floating-point types 、 Constant evaluation
1： Yes double Type of precision within and float、double Operation within the scope of type 、 Constant evaluation , Yes long double Within the scope of long double Type of operation 、 Constant evaluation
2： For all floating point type ranges and long double Operations and constant evaluation within type precision

Round off

float.h Macro in FLT_ROUNDS Determines how the system handles rounding , The rounding scheme corresponding to the specified value is as follows ：

-1： Not sure
0： Zero truncation
1： Round to the nearest value
2： The trend is endless
3： Towards infinity

The system can define other values , Corresponding to other rounding schemes , Some systems provide schemes to control rounding , under these circumstances ,fenv.h Medium festround() Function provides programming control .

In decimal ,8/10 or 4/5 Can accurately represent 0.8. But most computer systems store results in binary , In binary ,4/5 Expressed as a wireless circular decimal ：
0.1100110011001100…
therefore , In the 0.8 Stored in the x In the middle of the day , Round it to an approximate value , Its specific value depends on the rounding scheme used .

Some implementations may not be satisfied IEC 60559 The requirements of , for example , The underlying hardware may not meet the requirements , therefore ,C99 Defines two macros that can be used as preprocessor instructions , Check whether the implementation conforms to the specification .

fenv.h The header file

Provides some ways to interact with floating-point environments . Allows the user to set the float control mode value （ This value manages how floating-point operations are performed ） And determine the floating point status criteria （ Or abnormal ） Value （ Report information about the operation effect ）, for example , The control mode setting specifies the rounding scheme ： Set a status flag if floating-point overflow occurs in the operation , The operation of setting the status flag is called throwing an exception .

       
       #pragma STDC FENV_ACCESS ON 
       
       //  Turn on support 
       
       #pragma STDC FENV_ACCESS OFF 
       
       //  Turn off support 
       
       #pragma STDC FENV_ACCESS DEFAULT 
       
       //  default setting 
      
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STDC FP_CONTRACT Compilation instructions

FPU You can combine floating-point expressions of multiple operators into one operation , But you can calculate them independently .

       
       #pragma STDC FP_CONTRACT OFF
       
       val 
       
       = 
       
       X 
       
       * 
       
       Y 
       
       - 
       
       Z;
       
       #pragma STDC FP_CONTRACT ON
      
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The math library broke its heart for floating-point arithmetic

For example look ,

C90 Math library ：

       
       double 
       
       sin(
       
       double);
       
       double 
       
       sqrt(
       
       double);
      
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C99 and C11 The library provides... For all these functions float The type and long double Function of type ：

       
       float 
       
       sinf(
       
       float);
       
       long 
       
       double 
       
       sinl(
       
       long 
       
       double);
      
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2.

loglp(x) The value represented is the same as log(1+x) identical , however loglp(x) Different algorithms are used , For the smaller x The calculation is more accurate . therefore , have access to log() Function to perform ordinary operations , But for the high precision and x It's worth less , use loglp() Functions are better .

Computer system structure

Compare real numbers with floating-point numbers

	Scope of representation	Representation precision	Uniqueness
The set of real Numbers	Infinite	continuity	No redundant
Floating point numbers	Co., LTD.	Discontinuous	redundancy

Floating point table number range

【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _10

A floating-point data representation requires 6 Two parameters to define .
Two numerical ：
m： The value of the mantissa , It also includes the code system of mantissa （ Original code or complement code ） And number system （ Decimal or integer ）;
e： The value of the step code , Generally, code shift is adopted （ Also called partial code 、 Add code 、 I'll wait ） Or complement , Integers ;
Two bases ：
【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _11 ： Base of mantissa , There are usually binary , Quaternary 、 octal 、 Hexadecimal and decimal, etc ;
： The basis of the order code , Of all the floating-point data representations seen so far , 【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _12 Are all 2.
Two words long ：
p： Mantissa length , there p It does not refer to the binary digits of mantissa , When when , Every time 4 Binary bits represent one bit mantissa ;
q： Order code length , Because the basis of the order code is usually 2, therefore , In general ,q Is the binary digits of the order code part .

In the computer , Data is always stored in storage units , And the word length of the computer's storage unit is limited , therefore , The number and range of floating-point numbers that any floating-point representation can represent are limited .

Use the original code in the mantissa 、 Pure decimal , The order code adopts code shift 、 Floating point representation of integers , Normalize floating-point numbers N The range of tables is ：

【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _15

Mantissa in original code 、 Normalize the table number range of floating-point numbers in pure decimal

Table number range	Normalized mantissa	Order code	Normalize floating-point numbers
Maximum positive number
Minimum positive number
Maximum negative number
Minimum negative number

When the mantissa is represented by a complement , The table number range of the positive number interval is exactly the same as that of the mantissa when the original code is used , The table number range of negative number interval is ：

【 Course assignments 】 Floating point operation analysis and precision improvement _ Floating point numbers _16

Mantissa complement 、 Normalize the table number range of floating-point numbers in pure decimal

Table number range	Normalized mantissa	Order code	Normalize floating-point numbers
Maximum positive number
Minimum positive number
Maximum negative number
Minimum negative number

Floating point table number precision

Table number precision is also called table number error , The fundamental reason for the table count accuracy of floating-point numbers is the discontinuity of floating-point numbers , Because the word length of any floating-point representation is always limited . If the word length is 32 position , The maximum number of floating-point numbers that can be represented by this floating-point representation is 【 Course assignments 】 Floating point operation analysis and precision improvement _ Floating point numbers _37 individual , In mathematics, real numbers are continuous , It has an infinite number of , therefore , A representation of floating-point numbers. The number of floating-point numbers that can be represented is only a small part of real numbers , That is, a subset of it , We call it the set of floating-point numbers for this floating-point representation .

Floating point set F The table number error of can be defined as , Make N Is a set of floating point numbers F Any given real number in , and M yes F Is closest to N, And used instead of N Floating point number , Then the absolute table number error ：【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _38 , Relative table error .

Because in the same floating-point representation , The length of the mantissa significant bit of the normalized floating-point number is determined , therefore , The relative table count error of normalized floating-point numbers is determined , Because the distribution of normalized floating-point numbers on the number axis is uneven , Therefore, its absolute table error is uncertain . This is the opposite of the way fixed-point numbers are expressed , The absolute error of the fixed-point number representation is definite , The relative error is uncertain .

There are two direct causes of floating point number table error , One is two floating point numbers a and b Are in a floating-point set of some kind of floating-point representation , and a and b The result of the operation may not be in this floating-point set , Therefore, it must be represented by a floating-point number closest to it in this floating-point set , Thus, the table number error is caused . The other is to convert data from decimal to binary 、 Quaternary 、 Error may occur in octal or hexadecimal .

The first reason ：
give an example ,q=1,p=2,r_m=2,r_e=2
All the floating-point numbers that can be represented by a floating-point representation

	1(1.1)	0(1.0)	-1(0.1)	-2(0.0)
0.75(0.11)	3/2	3/4	3/8	3/16
0.5(0.10)	1	1/2	1/4	1/8
-0.5(1.10)	-1	-1/2	-1/4	-1/8
-0.75(1.11)	-3/2	-3/4	-3/8	-3/16

If there are two floating point numbers ：a₁=1/2,b₁=3/4 Within the defined floating point set , And the result of their addition ：a₁+b₁=5/4, Is not in this set of floating point numbers , To do this, you must use the nearest in the set of floating-point numbers 5/4 Floating point number 1 or 3/2 Instead of

So as to generate absolute meter number error ：
【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _40 or

The relative table error is ：
【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _42 or

Another reason ： The process of raw data entering the computer from the outside , Generally speaking, data is converted from decimal system to binary system 、 Quaternary 、 Octal or hexadecimal, etc . Because the length of the physical data storage unit is always limited , Thus, table number error may occur .

Put the decimal number 0.1 In a binary computer , Circular decimals appear , namely ：
0.1₍₁₀₎

=0.000110011001100···₍₂₎

=0.0121212···₍₄₎

=0.06146314···₍₈₎

=0.1999···₍₁₆₎

The mantissa of finite length cannot be used to express this number accurately , So there is a problem how to deal with the lower part of the mantissa that cannot be expressed , It is usually rounded . There are many ways to round , Different rounding methods produce different table number errors . In order to obtain as high table count accuracy as possible within a limited tail number length , Rounding must be done after normalization .
0.1₍₁₀₎

=0.110011001100···₍₂₎x2^-3

=0.121212···₍₄₎x4^-1

=0.6146314···₍₈₎x8^-1

=0.1999···₍₁₆₎x16⁰

Decimal system 0.1 Multiple representations of floating point numbers ：

Base value of mantissa	Step symbol	Order code	Tail symbol	mantissa
r_m=2	0	101	0	1100110011001100
r_m=4	0	111	0	0110011001100110
r_m=8	0	111	0	1100110011001100
r_m=16	1	000	0	0001100110011001

For different mantissa base values r_m, The lower part of the mantissa to be processed during rounding is also different , The final result after rounding will not be the same . In general , With the mantissa base value r_m The increase of , The prefix in the normalized binary mantissa "0" The more you have . Only in the mantissa base value r_m=2 when , To ensure that there is no prefix in the normalized mantissa "0", So as to obtain the highest accuracy of table number .

The table number precision of specifications and floating-point numbers is mainly related to the mantissa base value r_m And mantissa length p of , In general , It is considered that the accuracy of the last digit of the normalized mantissa is half , such , The table count precision of normalized floating-point numbers can be expressed as follows ：

【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _44

When r_m>2 when ,

【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _45

Conclusion ： When the mantissa length of a floating-point number （ Refers to the number of binary digits ） Phase at the same time , Mantissa base value r_m, take 2 It has the highest precision of table number . When the mantissa base value r_m>2 when , The table number precision of floating-point numbers is the same as that of r_m=2 It will cost 【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _46 times , Equivalent to the mantissa reduced Binary bits .

《 Design and implementation of fast single precision floating point arithmetic unit 》

Tianhongli , Yan Huiqiang , Zhaohongdong . Design and implementation of fast single precision floating point arithmetic unit [J]. Journal of Hebei University of technology ,2011,40(03):74-78.

The arithmetic unit is CPU An important part of , As a typical PC Computers generally have at least one fixed-point arithmetic unit , stay 586 In previous models , Due to the limitation of hardware conditions and technology at that time , Floating point arithmetic units generally appear in the form of coprocessors ,90 After the age , With the development of hardware technology , Floating point arithmetic unit FPU Can be integrated into CPU Inside , among FPGA（ Field programmable gate array ） Technology makes it a reality .

In the development of computer systems , The most widely used representation of real numbers is the floating-point representation . Floating point numbers, on the other hand , There are two forms ： Denormalized and normalized floating point operations . Normalized floating point operation requirements ： Before operation 、 After the operation, they are normalized numbers , This operation can obtain the largest significant number in the computer , Therefore, standardized floating-point operations are generally used in computers .

This paper designs a fast single precision floating-point arithmetic unit ：

【 Course assignments 】 Floating point operation analysis and precision improvement _ Floating point numbers _48

Single precision floating-point number normalization and data type discrimination module ： According to the form of input data , Normalize the mantissa .
Single precision floating-point addition and subtraction preprocessing module : When two normalized single precision floating-point numbers are added or subtracted , First, we must deal with the order code and symbol . Order code division , To align , That is to say, the small order is in line with the large order . The sign of the result of the operation is consistent with that of the largest floating-point number .
Parallel single precision floating-point addition and subtraction module ： Whether you add or subtract , It is necessary to use complement plus operation to complete . When two numbers are added together , Add directly with the complement of two numbers . When two numbers are subtracted , The subtracted number takes the complement directly , Subtract first and negate , Then find the complement , Then add the two numbers . The adder is realized by fast parallel addition .
Single precision floating-point multiplication or division preprocessing module ： When two floating-point numbers are multiplied or divided , The sign of the operation result is equal to the XOR operation of the two number symbols involved in the operation . The order code value of the operation result is ： Multiplication E=E 1 +E 2 -7FH; In addition to the operation E=E 1 -E 2 +7FH.(E The order code of the result ,E1 Is the order code of the multiplicand or divisor ,E 2 Is the order code of a multiplier or divisor ).
Parallel single precision floating point multiplication module ： For this multiplier, the unsigned 24*24 Parallel array multiplier , This design method will greatly shorten the time required for multiplication .
Parallel single precision floating-point division module ： Prejudgment 0, If the divisor is 0, Then send an error message ; otherwise , Let the dividend and divisor go directly into the parallel divider ( Do not restore the remainder array divider ) In the process of operation .
Operation result processing module ： Round the result of the operation 、 normalized 、 Identify data types and other operations .

《 be based on FPGA Research on floating point arithmetic unit 》

Dadandan . be based on FPGA Research on floating point arithmetic unit [D]. Inner Mongolia University ,2012.

1. introduction

【 Course assignments 】 Floating point operation analysis and precision improvement _ Floating point numbers _49
FPGA（ Field programmable gate array ）

It is a configurable logic block matrix and connected by programmable interconnection IOB A programmable semiconductor composed of . Using hardware description language to describe the function of digital system , After a series of conversion procedures 、 Automatic placement and routing 、 Simulation and other processes ; Finally, generate the configuration FPGA Device data file and download to FPGA in . In this way, the application specific integrated circuit meeting the requirements of users is realized , It really achieves the goal of self-designed by users 、 The purpose of developing and producing integrated circuits by oneself .

【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _50

2. Floating point means

According to whether the position of the decimal point is fixed , It is divided into fixed-point representation and floating-point representation .

The representation range of floating-point numbers is mainly determined by the number of bits of the order code , The precision of significant digits is mainly determined by the digits of mantissa .

Normalize floating-point numbers

【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _51

3. Research on normalized floating point number basic operation and hardware circuit

Floating point addition and subtraction

General operation rules of floating point operation ：

Z=X+Y=-0.5+3.375=2.875, explain IEEE754 Floating point addition process .

X=-0.5,1.0x2^-1,s=1,e=-1+127=126,f=0. Use this method to find Y.
Y=3.375, 1.1011x2¹,s=0,e=1,f=1011.

X： 1 01111110 00000000000000000000000
Y： 0 10000000 10110000000000000000000

because X and Y Different order codes , Mantissa cannot be subtracted directly , To put X The mantissa of is shifted to the right 2 position , such X and Y You have the same order code .

0 10000000 01110000000000000000000（2.875）

The general addition and subtraction operation steps of normalized floating-point numbers must go through the opposite order 、 Add or subtract the mantissa 、 Mantissa normalization 、 Round off 、 There are several steps to judge the overflow .

Antithetic order

【 Course assignments 】 Floating point operation analysis and precision improvement _ Floating point numbers _52

【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _53
2. Add or subtract the mantissa

【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _54
3. Mantissa normalization

【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _55
4. Round off 、 Judgment overflows

【 Course assignments 】 Floating point operation analysis and precision improvement _ Floating point numbers _56
Multiplication floating point operation

【 Course assignments 】 Floating point operation analysis and precision improvement _ Arithmetic unit _57

Floating point division

【 Course assignments 】 Floating point operation analysis and precision improvement _ floating-point _59

《 be based on RISC-V Floating point instruction set FPU Research and design of 》

[1] Panshupeng , Liuyouyao , Jiaojiye , Li Zhao . be based on RISC-V Floating point instruction set FPU Research and design of [J/OL]. Computer engineering and Application :1-10[2021-01-02].http://kns.cnki.net/kcms/detail/11.2127.TP.20201112.1858.002.html.

Floating point processor as an accelerator , Working in parallel with integer pipeline , And share large-scale computing from the main processor 、 High latency floating point instructions . If these instructions are executed by the main processor , Because of floating-point division 、 Operations such as square root, multiplication and accumulation require a lot of computation and a long waiting time , It will slow down the main processor , There will also be a loss of accuracy , So floating-point processors help speed up the entire chip .

Many embedded processors , Especially some traditional designs , Floating point operations are not supported . Many functions of floating-point processors can be emulated through software libraries , Although it saves the additional hardware cost of floating-point processors , But the speed is obviously slower , Not enough to meet the real-time requirements of embedded processors . In order to improve the performance of the system , The floating-point processor needs to be implemented in hardware .

《 Research on high precision algorithm based on multi part floating point representation 》

[1] Du peibing . Research on high precision algorithm based on multi part floating point representation [D]. National Defense University of science and technology ,2017.

The history of floating point numbers

The definition of floating point numbers

Problems with floating point numbers

《 be based on FPGA Research and implementation of single and double precision floating point arithmetic unit based on 》

[1] Wangjingwu . be based on FPGA Research and implementation of single and double precision floating point arithmetic unit based on [D]. Xi'an Petroleum University ,2017.

1.2 Research status of floating point arithmetic units at home and abroad

stay CPU The initial stage of structural design , Floating point arithmetic units are not integrated in CPU Internal , It is a separate chip called a floating point coprocessor , Its function is to assist CPU Process data together . from Intel Of 80486DX CPU Start ,FPU Was integrated into CPU Inside .

2.1 Floating point format parsing

The third chapter Research on Algorithm of floating point arithmetic unit

3.1 Floating point addition and subtraction

3.2 Floating point multiplication

3.3 Floating point division

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