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Computer Engineering FloatingPoint page 1 Floating Point Number system corresponding to the decimal notation 1,837 * 10 significand exponent A great number of corresponding binary standards exists. There is one common standard: IEEE 754-1985 (IEC 559) 4

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Computer Engineering FloatingPoint page 2 IEEE 754-1985 Number representations: –Single precision (32 bits) sign:1 bit exponent:8 bits fraction:23 bits –Double precision (64 bits) sign:1 bit exponent:11 bits fraction:52 bits

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Computer Engineering FloatingPoint page 3 Single Precision Format 1823 Sign S Exponent E: excess 127 binary integer Mantissa M (24 bit): normalized binary significand w/ hidden integer bit: 1.F Excess 127; actual exponent is e = E - 127 N = (-1) S * (1.F [bit-string])*2 e SEF

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Computer Engineering FloatingPoint page 4 Example 1 SEF e = E - 127 e = 126 - 127 = -1 N = (-1) 1 * (1.1 [bit-string]) *2 -1 N = -1 * 0.11 [bit-string] N = -1 * (2 -1 *1 + 2 -2 *1) N = -1 * (0.5*1 + 0.25*1) = -0.75 10111111010000000000000000000000

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Computer Engineering FloatingPoint page 5 Single Precision Range Magnitude of numbers that can be represented is in the range: 2 -126 *(1.0) to 2 128 *(2-2 - 23 ) which is approximately: 1.2*10 -38 to 3.4 *10 38

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Computer Engineering FloatingPoint page 6 IEEE 754-1985 Single Precision (32 bits) Fraction part: 23 bits; 0 x < 1 Significand: 1 + fraction part. “1” is not stored; “hidden bit”. Corresponds to 7 decimal digits. Exponent: 127 added to the exponent. Corresponds to the range 10 -39 to 10 39 Double Precision (64 bits) Fraction part: 52 bits; 0 x < 1 Significand: 1 + fraction part. “1” is not stored; “hidden bit”. Corresponds to 16 decimal digits. Exponent: 1023 added to the exponent; Corresponds to the range 10 -308 to 10 308

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Computer Engineering FloatingPoint page 7 IEEE 754-1985 Special features: –Correct rounding of “halfway” result (to even number). –Includes special values: NaNNot a number Infinity - - Infinity –Uses denormal number to represent numbers less than 2 -E min –Rounds to nearest by default; Three other rounding modes exist. –Sophisticated exception handling.

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Computer Engineering FloatingPoint page 8 Add / Sub (s1 * 2 e1 ) +/- (s2 * 2 e2 ) = (s1 +/- s2) * 2 e3 = s3 * 2 e3 –s = 1.s, the hidden bit is used during the operation. 1: Shift summands so they have the same exponent: –e.g., if e2 < e1: shift s2 right and increment e2 until e1 = e2 2: Add/Sub significands using the sign bits for s1 and s2. –set sign bit accordingly for the result. 3: Normalize result (sign bit kept separate): –shift s3 left and decrement e3 until MSB = 1. 4: Round s3 correctly. –more than 23 / 52 bits is used internally for the addition.

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Computer Engineering FloatingPoint page 9 Multiplication (s1 * 2 e1 ) * (s2 * 2 e2 ) = s1 * s2 * 2 e1+e2 so, multiply significands and add exponents. Problem: Significand coded in sign & magnitude; use unsigned multiplication and take care of sign. Round 2n bits significand to n bits significand. Normalize result, compute new exponent with respect to bias.

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Computer Engineering FloatingPoint page 10 Division (s1 * 2 e1 ) / (s2 * 2 e2 ) = (s1 / s2) * 2 e1-e2 so, divide significands and subtract exponents Problem: Significand coded in signed- magnitude - use unsigned division (different algoritms exists) and take care of sign Round n + 2 (guard and round) bits significand to n bits significand Compute new exponent with respect to bias

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Computer Arithmetic Floating Point. We need a way to represent –numbers with fractions, e.g., 3.1416 –very small numbers, e.g.,.000000001 –very large.

Computer Arithmetic Floating Point. We need a way to represent –numbers with fractions, e.g., 3.1416 –very small numbers, e.g.,.000000001 –very large.

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