Emulating Binary-Level Operations and Number Representations

Introduction

At the heart of every computer lies a simple truth: everything is binary. Whether you are working with integers, floating-point numbers, or characters, all data is ultimately represented as sequences of 0s and 1s.

To truly understand how computers operate, developers must go beyond high-level abstractions and study how numbers are represented and manipulated at the binary level. This article covers three pillars of low-level computation:

1. Bitwise operations (AND, OR, XOR, NOT, shifts).
2. Signed integer representations (one’s complement and two’s complement).
3. Floating-point numbers (IEEE 754 standard).

1. Bitwise Operations

Bitwise operators act directly on the binary digits of numbers.

AND (&)

1101   (13)
1011   (11)
----
1001   (9)

Use case: masking specific bits.

OR (|)

1101   (13)
1011   (11)
----
1111   (15)

Use case: setting specific bits.

XOR (^)

1101   (13)
1011   (11)
----
0110   (6)

Use case: toggling bits, cryptographic primitives.

NOT (~)

00001101   (13)
11110010   (interpreted as -14 in signed context)

Use case: flipping all bits.

Shifts

• Left shift (<<): multiply by 2.
• Right shift (>>): divide by 2 (arithmetic vs logical depending on signedness).

00001101 (13)
<< 1 → 00011010 (26)
>> 1 → 00000110 (6)

2. Signed Integer Representations

Unsigned integers are straightforward: every bit is a power of 2. For example, in 8 bits:

00001101 = 13
11111111 = 255

But integers can also be negative, and this requires special encoding. Historically, two major approaches existed: one’s complement and two’s complement.

One’s Complement Representation

In one’s complement, the negative of a number is obtained by inverting all the bits of its positive form.

+13 = 00001101
-13 = 11110010

Problem: it introduces two zeros:

+0 = 00000000
-0 = 11111111

This redundancy complicates arithmetic and equality checks, which is why it was eventually abandoned.

Two’s Complement Representation (modern standard)

Two’s complement solves the zero problem and is now universally used.

1. Write the positive value in binary.
2. Invert all bits.
3. Add 1.

+13 = 00001101
invert → 11110010
add 1 → 11110011

So:

00001101 = +13
11110011 = -13

Value Ranges

• Unsigned: 0 → 2^n - 1
• One’s complement signed: -(2^(n-1)-1) → +(2^(n-1)-1) (two zeros)
• Two’s complement signed: -2^(n-1) → 2^(n-1) - 1

Comparison Table (4-bit example)

3. Floating-Point Representation: IEEE 754

Integers are simple compared to real numbers like 3.14. To standardize floating-point arithmetic, computers use the IEEE 754 standard.

A floating-point number is stored as:

(-1)^sign × 1.mantissa × 2^(exponent - bias)

Single Precision (32 bits)

[ Sign (1) ] [ Exponent (8) ] [ Mantissa (23) ]

Example: 13.25

1101.01 = 1.10101 × 2^3
Sign = 0
Exponent = 3 + 127 = 130 = 10000010
Mantissa = 10101...

0 10000010 10101000000000000000000

Double Precision (64 bits)

[ Sign (1) ] [ Exponent (11) ] [ Mantissa (52) ]

• Greater range and precision (≈15 decimal digits vs ≈7 for single precision).
• Default choice in scientific computing.

Special Values

• NaN (Not a Number)
• +∞ and -∞
• Subnormal numbers for very small magnitudes

Why This Matters

• Systems programming: knowing two’s complement prevents overflow errors.
• Historical insight: one’s complement explains quirks on early machines.
• Cryptography: XOR and shifts are core building blocks.
• Numerical analysis: IEEE 754 rounding must be understood to ensure accuracy.
• Debugging: raw memory can mean signed int, unsigned int, or float.

Conclusion

Binary-level operations are the backbone of all computing. Historically, integers could be represented in one’s complement (with two zeros, now obsolete) or two’s complement (the universal standard today). Floating-point numbers, meanwhile, follow the IEEE 754 specification, guaranteeing consistency across platforms.

By mastering these representations and operations, developers gain a deeper understanding of how code interacts with hardware — a critical skill for systems programming, cryptography, and scientific computing.

Further Exploration

To go beyond theory, I’ve built a C++ library from scratch that emulates all these operations — from bitwise logic to number representations. You can explore it here:

Numero on GitHub