Jan 292017
 

Binary NumbersAbraham Lincoln once famously said, “Everybody loves a compliment.”  I suspect that if he had been a mathematician he would have loved complements, too. We’ve already seen what complements are and talked about the two most prolific: the radix complement and the diminished radix complement. Now it’s time to explore how we can leverage complements to do some really interesting integer arithmetic. Using complements we can subtract one positive integer from another or add a negative integer to a positive one by simply performing addition with two positive integers. The algorithm behind this black magic is called the Method of Complements.

Continue reading »

Jan 102017
 

Binary NumbersIn my last post about binary signed integers, I introduced the ones complement representation. At the time, I said that the ones complement was found by taking the bitwise complement of the number. My explanation about how to do this was simple: invert each bit, flipping 1 to 0 and vice versa. While it’s true that this is all you need to know in order to determine the ones complement of a binary number, if you want to understand how computers do arithmetic with signed integers and why they represent them the way they do, then you need to understand what complements are and how the method of complements allows computers to subtract one integer from another, or add a positive and negative integer, by doing addition with only positive integers.

Continue reading »

Jul 262016
 

Binary NumbersIn the last post, we saw that one of the major failings of the signed magnitude representation was that addition and subtraction could not be performed on the same hardware as for unsigned integers. As I pointed out, the reason for this is because negating a number in signed magnitude does not yield the additive inverse of that number. The ones complement representation eliminates this issue, although it does introduce new, subtle issues, and [spoiler] doesn’t address the problem of having two representations for zero.

Continue reading »

Mar 092016
 

This is going to be another one of my “selfish” posts – written primarily for me to refer back to in the future and not because I believe it will benefit anyone other than me. The idea is one that I always took for granted but had a hard time proving to myself once I decided to try.

Theorem: Suppose we have an M bit unsigned binary integer with value A. Consider the first (least significant) N bits with value B. Then:

A \equiv B \bmod{2^N}

Put another way, arithmetic with unsigned binary integers of a fixed length N is always performed modulo 2^N.
Continue reading »

Feb 012016
 

Binary NumbersI previously discussed the signed magnitude solution to representing signed integers as binary strings and pointed out that while it had the advantage of being simple, it also has some disadvantages. For starters, N-bit signed magnitude integers have two representations for zero: positive zero (a bitstring with N zeros) and negative zero (a bitstring with a one followed by N-1 zeros).

There is another significant disadvantage that isn’t obvious until you try to implement signed magnitude representation in silicon. Specifically, you can’t do mathematics with signed magnitude integers using the same hardware as is used for unsigned integers.

Continue reading »

Nov 182015
 

Binary NumbersWe humans and our meat computers don’t have any trouble recognizing the sign of a number. If there is a minus sign, “-,” in front of a number, that number is negative. If a number is prefixed by a plus sign, “+,” or, the more likely case, has no prefix at all, then the number is positive.

Computers of the silicon kind don’t have it so easy. They don’t have the luxury of pluses and minuses to tell them the sign of a number. All they have are zeros and ones, the alphabet of binary systems. So what is their solution?

Continue reading »