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<h1 id="33-number-encoding">3.3 &nbsp; Number Encoding *<a class="headerlink" href="#33-number-encoding" title="Permanent link">&para;</a></h1>
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<h2 id="331-sign-magnitude-1s-complement-and-2s-complement">3.3.1 &nbsp; Sign-Magnitude, 1's Complement, and 2's Complement<a class="headerlink" href="#331-sign-magnitude-1s-complement-and-2s-complement" title="Permanent link">&para;</a></h2>
<p>In the table from the previous section, we found that all integer types can represent one more negative number than positive numbers. For example, the <code>byte</code> range is <span class="arithmatex">\([-128, 127]\)</span>. This phenomenon is counterintuitive, and its underlying reason involves knowledge of sign-magnitude, 1's complement, and 2's complement.</p>
<p>First, it should be noted that <strong>numbers are stored in computers in the form of "2's complement"</strong>. Before analyzing the reasons for this, let's first define these three concepts.</p>
<ul>
<li><strong>Sign-magnitude</strong>: We treat the highest bit of the binary representation of a number as the sign bit, where <span class="arithmatex">\(0\)</span> represents a positive number and <span class="arithmatex">\(1\)</span> represents a negative number, and the remaining bits represent the value of the number.</li>
<li><strong>1's complement</strong>: The 1's complement of a positive number is the same as its sign-magnitude. For a negative number, the 1's complement is obtained by inverting all bits except the sign bit of its sign-magnitude.</li>
<li><strong>2's complement</strong>: The 2's complement of a positive number is the same as its sign-magnitude. For a negative number, the 2's complement is obtained by adding <span class="arithmatex">\(1\)</span> to its 1's complement.</li>
</ul>
<p>Figure 3-4 shows the conversion methods among sign-magnitude, 1's complement, and 2's complement.</p>
<p><a class="glightbox" href="../number_encoding.assets/1s_2s_complement.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="Conversions among sign-magnitude, 1's complement, and 2's complement" class="animation-figure" src="../number_encoding.assets/1s_2s_complement.png" /></a></p>
<p align="center"> Figure 3-4 &nbsp; Conversions among sign-magnitude, 1's complement, and 2's complement </p>
<p><u>Sign-magnitude</u>, although the most intuitive, has some limitations. On one hand, <strong>the sign-magnitude of negative numbers cannot be directly used in operations</strong>. For example, calculating <span class="arithmatex">\(1 + (-2)\)</span> in sign-magnitude yields <span class="arithmatex">\(-3\)</span>, which is clearly incorrect.</p>
<div class="arithmatex">\[
\begin{aligned}
&amp; 1 + (-2) \newline
&amp; \rightarrow 0000 \; 0001 + 1000 \; 0010 \newline
&amp; = 1000 \; 0011 \newline
&amp; \rightarrow -3
\end{aligned}
\]</div>
<p>To solve this problem, computers introduced <u>1's complement</u>. If we first convert sign-magnitude to 1's complement and calculate <span class="arithmatex">\(1 + (-2)\)</span> in 1's complement, then convert the result back to sign-magnitude, we can obtain the correct result of <span class="arithmatex">\(-1\)</span>.</p>
<div class="arithmatex">\[
\begin{aligned}
&amp; 1 + (-2) \newline
&amp; \rightarrow 0000 \; 0001 \; \text{(Sign-magnitude)} + 1000 \; 0010 \; \text{(Sign-magnitude)} \newline
&amp; = 0000 \; 0001 \; \text{(1's complement)} + 1111 \; 1101 \; \text{(1's complement)} \newline
&amp; = 1111 \; 1110 \; \text{(1's complement)} \newline
&amp; = 1000 \; 0001 \; \text{(Sign-magnitude)} \newline
&amp; \rightarrow -1
\end{aligned}
\]</div>
<p>On the other hand, <strong>the sign-magnitude of the number zero has two representations, <span class="arithmatex">\(+0\)</span> and <span class="arithmatex">\(-0\)</span></strong>. This means that the number zero corresponds to two different binary encodings, which may cause ambiguity. For example, in conditional judgments, if we don't distinguish between positive zero and negative zero, it may lead to incorrect judgment results. If we want to handle the ambiguity of positive and negative zero, we need to introduce additional judgment operations, which may reduce the computational efficiency of the computer.</p>
<div class="arithmatex">\[
\begin{aligned}
+0 &amp; \rightarrow 0000 \; 0000 \newline
-0 &amp; \rightarrow 1000 \; 0000
\end{aligned}
\]</div>
<p>Like sign-magnitude, 1's complement also has the problem of positive and negative zero ambiguity. Therefore, computers further introduced <u>2's complement</u>. Let's first observe the conversion process of negative zero from sign-magnitude to 1's complement to 2's complement:</p>
<div class="arithmatex">\[
\begin{aligned}
-0 \rightarrow \; &amp; 1000 \; 0000 \; \text{(Sign-magnitude)} \newline
= \; &amp; 1111 \; 1111 \; \text{(1's complement)} \newline
= 1 \; &amp; 0000 \; 0000 \; \text{(2's complement)} \newline
\end{aligned}
\]</div>
<p>Adding <span class="arithmatex">\(1\)</span> to the 1's complement of negative zero produces a carry, but since the <code>byte</code> type has a length of only 8 bits, the <span class="arithmatex">\(1\)</span> that overflows to the 9<sup>th</sup> bit is discarded. That is to say, <strong>the 2's complement of negative zero is <span class="arithmatex">\(0000 \; 0000\)</span>, which is the same as the 2's complement of positive zero</strong>. This means that in 2's complement representation, there is only one zero, and the positive and negative zero ambiguity is thus resolved.</p>
<p>One last question remains: the range of the <code>byte</code> type is <span class="arithmatex">\([-128, 127]\)</span>, and how is the extra negative number <span class="arithmatex">\(-128\)</span> obtained? We notice that all integers in the interval <span class="arithmatex">\([-127, +127]\)</span> have corresponding sign-magnitude, 1's complement, and 2's complement, and sign-magnitude and 2's complement can be converted to each other.</p>
<p>However, <strong>the 2's complement <span class="arithmatex">\(1000 \; 0000\)</span> is an exception, and it does not have a corresponding sign-magnitude</strong>. According to the conversion method, we get that the sign-magnitude of this 2's complement is <span class="arithmatex">\(0000 \; 0000\)</span>. This is clearly contradictory because this sign-magnitude represents the number <span class="arithmatex">\(0\)</span>, and its 2's complement should be itself. The computer specifies that this special 2's complement <span class="arithmatex">\(1000 \; 0000\)</span> represents <span class="arithmatex">\(-128\)</span>. In fact, the result of calculating <span class="arithmatex">\((-1) + (-127)\)</span> in 2's complement is <span class="arithmatex">\(-128\)</span>.</p>
<div class="arithmatex">\[
\begin{aligned}
&amp; (-127) + (-1) \newline
&amp; \rightarrow 1111 \; 1111 \; \text{(Sign-magnitude)} + 1000 \; 0001 \; \text{(Sign-magnitude)} \newline
&amp; = 1000 \; 0000 \; \text{(1's complement)} + 1111 \; 1110 \; \text{(1's complement)} \newline
&amp; = 1000 \; 0001 \; \text{(2's complement)} + 1111 \; 1111 \; \text{(2's complement)} \newline
&amp; = 1000 \; 0000 \; \text{(2's complement)} \newline
&amp; \rightarrow -128
\end{aligned}
\]</div>
<p>You may have noticed that all the above calculations are addition operations. This hints at an important fact: <strong>the hardware circuits inside computers are mainly designed based on addition operations</strong>. This is because addition operations are simpler to implement in hardware compared to other operations (such as multiplication, division, and subtraction), easier to parallelize, and have faster operation speeds.</p>
<p>Please note that this does not mean that computers can only perform addition. <strong>By combining addition with some basic logical operations, computers can implement various other mathematical operations</strong>. For example, calculating the subtraction <span class="arithmatex">\(a - b\)</span> can be converted to calculating the addition <span class="arithmatex">\(a + (-b)\)</span>; calculating multiplication and division can be converted to calculating multiple additions or subtractions.</p>
<p>Now we can summarize the reasons why computers use 2's complement: based on 2's complement representation, computers can use the same circuits and operations to handle the addition of positive and negative numbers, without the need to design special hardware circuits to handle subtraction, and without the need to specially handle the ambiguity problem of positive and negative zero. This greatly simplifies hardware design and improves operational efficiency.</p>
<p>The design of 2's complement is very ingenious. Due to space limitations, we will stop here. Interested readers are encouraged to explore further.</p>
<h2 id="332-floating-point-number-encoding">3.3.2 &nbsp; Floating-Point Number Encoding<a class="headerlink" href="#332-floating-point-number-encoding" title="Permanent link">&para;</a></h2>
<p>Careful readers may have noticed: <code>int</code> and <code>float</code> have the same length, both are 4 bytes, but why does <code>float</code> have a much larger range than <code>int</code>? This is very counterintuitive because it stands to reason that <code>float</code> needs to represent decimals, so the range should be smaller.</p>
<p>In fact, <strong>this is because floating-point number <code>float</code> uses a different representation method</strong>. Let's denote a 32-bit binary number as:</p>
<div class="arithmatex">\[
b_{31} b_{30} b_{29} \ldots b_2 b_1 b_0
\]</div>
<p>According to the IEEE 754 standard, a 32-bit <code>float</code> consists of the following three parts.</p>
<ul>
<li>Sign bit <span class="arithmatex">\(\mathrm{S}\)</span>: occupies 1 bit, corresponding to <span class="arithmatex">\(b_{31}\)</span>.</li>
<li>Exponent bit <span class="arithmatex">\(\mathrm{E}\)</span>: occupies 8 bits, corresponding to <span class="arithmatex">\(b_{30} b_{29} \ldots b_{23}\)</span>.</li>
<li>Fraction bit <span class="arithmatex">\(\mathrm{N}\)</span>: occupies 23 bits, corresponding to <span class="arithmatex">\(b_{22} b_{21} \ldots b_0\)</span>.</li>
</ul>
<p>The calculation method for the value corresponding to the binary <code>float</code> is:</p>
<div class="arithmatex">\[
\text {val} = (-1)^{b_{31}} \times 2^{\left(b_{30} b_{29} \ldots b_{23}\right)_2-127} \times\left(1 . b_{22} b_{21} \ldots b_0\right)_2
\]</div>
<p>Converted to decimal, the calculation formula is:</p>
<div class="arithmatex">\[
\text {val}=(-1)^{\mathrm{S}} \times 2^{\mathrm{E} -127} \times (1 + \mathrm{N})
\]</div>
<p>The range of each component is:</p>
<div class="arithmatex">\[
\begin{aligned}
\mathrm{S} \in &amp; \{ 0, 1\}, \quad \mathrm{E} \in \{ 1, 2, \dots, 254 \} \newline
(1 + \mathrm{N}) = &amp; (1 + \sum_{i=1}^{23} b_{23-i} 2^{-i}) \subset [1, 2 - 2^{-23}]
\end{aligned}
\]</div>
<p><a class="glightbox" href="../number_encoding.assets/ieee_754_float.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="Calculation example of float under IEEE 754 standard" class="animation-figure" src="../number_encoding.assets/ieee_754_float.png" /></a></p>
<p align="center"> Figure 3-5 &nbsp; Calculation example of float under IEEE 754 standard </p>
<p>Observing Figure 3-5, given example data <span class="arithmatex">\(\mathrm{S} = 0\)</span>, <span class="arithmatex">\(\mathrm{E} = 124\)</span>, <span class="arithmatex">\(\mathrm{N} = 2^{-2} + 2^{-3} = 0.375\)</span>, we have:</p>
<div class="arithmatex">\[
\text { val } = (-1)^0 \times 2^{124 - 127} \times (1 + 0.375) = 0.171875
\]</div>
<p>Now we can answer the initial question: <strong>the representation of <code>float</code> includes an exponent bit, resulting in a range far greater than <code>int</code></strong>. According to the above calculation, the maximum positive number that <code>float</code> can represent is <span class="arithmatex">\(2^{254 - 127} \times (2 - 2^{-23}) \approx 3.4 \times 10^{38}\)</span>, and the minimum negative number can be obtained by switching the sign bit.</p>
<p><strong>Although floating-point number <code>float</code> expands the range, its side effect is sacrificing precision</strong>. The integer type <code>int</code> uses all 32 bits to represent numbers, and the numbers are evenly distributed; however, due to the existence of the exponent bit, the larger the value of floating-point number <code>float</code>, the larger the difference between two adjacent numbers tends to be.</p>
<p>As shown in Table 3-2, exponent bits <span class="arithmatex">\(\mathrm{E} = 0\)</span> and <span class="arithmatex">\(\mathrm{E} = 255\)</span> have special meanings, <strong>used to represent zero, infinity, <span class="arithmatex">\(\mathrm{NaN}\)</span>, etc.</strong></p>
<p align="center"> Table 3-2 &nbsp; Meaning of exponent bits </p>
<div class="center-table">
<table>
<thead>
<tr>
<th>Exponent Bit E</th>
<th>Fraction Bit <span class="arithmatex">\(\mathrm{N} = 0\)</span></th>
<th>Fraction Bit <span class="arithmatex">\(\mathrm{N} \ne 0\)</span></th>
<th>Calculation Formula</th>
</tr>
</thead>
<tbody>
<tr>
<td><span class="arithmatex">\(0\)</span></td>
<td><span class="arithmatex">\(\pm 0\)</span></td>
<td>Subnormal Number</td>
<td><span class="arithmatex">\((-1)^{\mathrm{S}} \times 2^{-126} \times (0.\mathrm{N})\)</span></td>
</tr>
<tr>
<td><span class="arithmatex">\(1, 2, \dots, 254\)</span></td>
<td>Normal Number</td>
<td>Normal Number</td>
<td><span class="arithmatex">\((-1)^{\mathrm{S}} \times 2^{(\mathrm{E} -127)} \times (1.\mathrm{N})\)</span></td>
</tr>
<tr>
<td><span class="arithmatex">\(255\)</span></td>
<td><span class="arithmatex">\(\pm \infty\)</span></td>
<td><span class="arithmatex">\(\mathrm{NaN}\)</span></td>
<td></td>
</tr>
</tbody>
</table>
</div>
<p>It is worth noting that subnormal numbers significantly improve the precision of floating-point numbers. The smallest positive normal number is <span class="arithmatex">\(2^{-126}\)</span>, and the smallest positive subnormal number is <span class="arithmatex">\(2^{-126} \times 2^{-23}\)</span>.</p>
<p>Double-precision <code>double</code> also uses a representation method similar to <code>float</code>, which will not be elaborated here.</p>
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