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Offset binary, [1] also referred to as excess-K, [1] excess-N, excess-e, [2] [3] excess code or biased representation, is a method for signed number representation where a signed number n is represented by the bit pattern corresponding to the unsigned number n+K, K being the biasing value or offset.
In the base −2 representation, a signed number is represented using a number system with base −2. In conventional binary number systems, the base, or radix, is 2; thus the rightmost bit represents 2 0, the next bit represents 2 1, the next bit 2 2, and so on. However, a binary number system with base −2 is also possible.
The double-precision binary floating-point exponent is encoded using an offset-binary representation, with the zero offset being 1023; also known as exponent bias in the IEEE 754 standard. Examples of such representations would be:
For a quad-precision number, the exponent is stored in the range 1 .. 32766 (0 and 32767 have special meanings), and is interpreted by subtracting the bias for a 15-bit exponent (16383) to get an exponent value in the range −16382 .. +16383.
The true significand of normal numbers includes 23 fraction bits to the right of the binary point and an implicit leading bit (to the left of the binary point) with value 1. Subnormal numbers and zeros (which are the floating-point numbers smaller in magnitude than the least positive normal number) are represented with the biased exponent value ...
The half-precision binary floating-point exponent is encoded using an offset-binary representation, with the zero offset being 15; also known as exponent bias in the IEEE 754 standard. [9] E min = 00001 2 − 01111 2 = −14; E max = 11110 2 − 01111 2 = 15; Exponent bias = 01111 2 = 15
Thus, in order to get the true exponent as defined by the offset-binary representation, the offset of 127 has to be subtracted from the value of the exponent field. The minimum and maximum values of the exponent field (00 H and FF H ) are interpreted specially, like in the IEEE 754 standard formats.
NAF assures a unique representation of an integer, but the main benefit of it is that the Hamming weight of the value will be minimal. For regular binary representations of values, half of all bits will be non-zero, on average, but with NAF this drops to only one-third of all digits.