Fixed point number usually allow only 8 bits (32 bit computing) of binary numbers for the fractional portion of the number which means many decimal numbers are recorded inaccurately. Floating Point numbers use exponents to shift the decimal point therefore they can store more accurate fractional values than fixed point numbers. However the CPU will have to perform extra arithmetic to read the number when stored in this format. Fixed point number usually allow only 8 bits (32 bit computing) of binary numbers for the fractional portion of the number which means many decimal numbers are recorded inaccurately. Floating Point numbers use exponents to shift the decimal point therefore they can store more accurate fractional values than fixed point numbers. However the CPU will have to perform extra arithmetic to read the number when stored in this format.
It's a tricky area: Decimal numbers can be represented exactly. In contrast, numbers like 1.1 do not have an exact representation in binary floating point. End users typically would not expect 1.1 to display as 1.1000000000000001 as it does with binary floating point. The exactness carries over into arithmetic. In decimal floating point, 0.1 + 0.1 + 0.1 - 0.3 is exactly equal to zero. In binary floating point, the result is 5.5511151231257827e-017. While near to zero, the differences prevent reliable equality testing and differences can accumulate. For this reason, decimal is preferred in accounting applications which have strict equality invariants. So you have to be carefull how you store floating point decimals in binary. It can also be used in a fraction. It must be simplufied then reduced and multiplied.
If you mean floating point number, they are significand, base and exponent.
the set of points equidistant from a fixed point
floating point operating per second
That is the Fulcrum.
fixed/floating point choice is an important ISA condition.
Finite precision arithmetic, solve numeric errors by using the floating point.
The advantages of integer arithmetic over floating point arithmetic is the absence of rounding errors. Rounding errors are an intrinsic aspect of floating point arithmetic, with the result that two or more floating point values cannot be compared for equality or inequality (or with other relational operators), as the exact same original value may be presented slightly differently by two or more floating point variables. Integer arithmetic does not show this symptom, and allows for simple and reliable comparison of numbers. However, the disadvantage of integer arithmetic is the limited value range. While scaled arithmetic (also known as fixed point arithmetic) allows for integer-based computation with a finite number of decimals, the total value range of a floating point variable is much larger. For example, a signed 32-bit integer variable can take values in the range -231..+231-1 (-2147483648..+2147483647), an IEEE 754 single precision floating point variable covers a value range of +/- 3.4028234 * 1038 in the same 32 bits.
mechanical, but it did do floating point arithmetic.
"Floating Point" refers to the decimal point. Since there can be any number of digits before and after the decimal, the point "floats". The floating point unit performs arithmetic operations on decimal numbers.
Fixed point overflow, Floating point overflow, Floating point underflow, etc.
The Z1 was a mechanical digital computer built by Konrad Zuse in Germany in the late 1930s. It was binary and did only floating point arithmetic. It had a Harvard Architecture. Hope that helps. The Z2 used the mechanical memory of the Z1, but replaced the mechanical floating point arithmetic unit with a relay integer arithmetic unit. The Z3 was all relays with only floating point arithmetic, but was destroyed by bombs in WW2. After WW2 he resumed making better and better computers.
The only arithmetic exception I can think of seeing has been caused by a division by zero statement. Trying to do integer division by 0 or mod 0 will result in this arithmetic exception. Note that floating point division by zero will result in "Infinity" being returned, and floating point modulus will result in "NaN" being returned.
The arithmetic and logic unit (ALU) within the computer's central processing unit (CPU) carries out arithmetic operations. Some designs also support a dedicated floating-point processing unit (FPU), which carries out arithmetic, trigonometric and logic operations based on floating-point variable types.
That's done by the ALU (arithmetic and logic unit).
The Z1 introduced the computer architecture on which modern computers are designed. The device was used to perform decimal floating point calculations during WWII.
in fixed point processor there is no separate mantissa and exponent part usually the nuumber can be represented from -1.000000to 1.0000000 wheras in floating point processor mantissa and exponent are separated so you can increase the range of values by compromising accuracy