FPGA vs Processor: Which Wins the Speed Race?

In this video, we're going to dive into the distinctions between FPGAs and processors.

Chances are, you've had some experience with processors like ARM, AVR, or DSP.

If not, you've probably experimented with programming for your computer, using languages such as C, BASIC, Pascal, or others.

Now, here's the thing: when you step into the world of FPGAs, you often bring along your programming mindset, and that can lead to some challenges.

Working with FPGAs requires more than just coding skills – you need a solid grasp of hardware design principles.

So, in this video, we're going to break down the differences between FPGAs and processors.

Our goal is to help you transition into the mindset of a digital hardware designer and leave behind programming-centric thinking.

In earlier videos, we delved into the fundamental nature and structure of FPGAs.

These aspects themselves reveal the key distinctions from processors.

Remember, an FPGA is essentially a collection of digital hardware resources that can be configured to create various digital circuits.

The approach involves breaking down a complex circuit into smaller parts, each of which is implemented using a Look-Up Table.

These individual LUTs are then interconnected using wires to create the desired circuit.

Now, let's connect the dots.

The Differences Between FPGAs and Processors

Based on what you've learned about FPGA nature and structure, we can highlight the key differences between FPGAs and processors.

1- No Pre-Built Hardware

The first major difference is this: when you work with processors, you're dealing with a CPU equipped with predefined instructions that it can execute as needed.

In contrast, with FPGAs, you don't start with specific hardware designed for particular operations.

Instead, you have a collection of hardware resources at your disposal.

2- Building Hardware vs Creating Software

Now, let's dig into the second difference between processors and FPGAs.

When you work with processors, you can define specific functions by writing programs.

Think of it like giving your processor a set of instructions to follow, and it executes those instructions one by one.

For instance, you can use pre-defined instructions for the processor and arrange them in a sequence to create a particular function.

However, with FPGAs, there's no concept of instructions because there's no CPU.

Instead, you design a specific function for an FPGA by properly connecting the available hardware resources within the FPGA.

So, when you're working with processors, it's like designing software, but when you're working with FPGAs, you're essentially designing hardware.

Now, let's tackle another significant difference.

3- Parallelism vs Sequential Execution

When working with processors, you can typically only perform one arithmetic or logic operation at a time.

In contrast, FPGAs give you the incredible ability to perform multiple logic or arithmetic operations simultaneously.

This magic happens thanks to the abundance of Look-Up Tables in FPGAs.

You can use several of these LUTs to implement a logic or arithmetic operation.

You can also implement other unrelated operations using different sets of LUTs.

Lastly, let's focus on the number of input-output pins.

4- Higher Number of Input-Output Pins

These ports are essential for communicating with peripherals, like external sensors or displays.

In processors, you're somewhat limited in the number of these ports available.

If you have many peripherals to connect, you may need to find ways to address them or share the ports through multiplexing.

However, in the world of FPGAs, you typically have a substantial number of these pins when you design interfacing with peripherals.

Of course, the exact count may vary depending on the type of FPGA you're working with, but generally, FPGAs offer a significantly higher number of input-output ports compared to processors.

Now, armed with these insights into the nature and differences between FPGAs and processors, we're ready to tackle a big question:

Why Are FPGAs So Much Faster Than Processors?

To answer this, we'll explore two fundamental reasons.

Let's break down the first reason why FPGAs are so much faster.

Parallelism

It all comes down to their incredible ability to perform a multitude of logic and arithmetic operations at the same time.

To get a clear picture, take a look at the two scenarios in this figure.

On the left side, you see a typical processor, where specific instructions are executed on specific data.

For instance, in this processor, there's a program to execute instruction C1 on data D1, then instruction C2 on data D2, and so on, all using the CPU.

Now, remember, a CPU can only execute one of these instructions at any given moment.

Now, consider the FPGA approach. Instead of relying on a single CPU, we tap into the FPGA's parallel processing capability.

Here's how it works: for each CPU instruction, like C1 and C2, we can create dedicated hardware components using sets of Look-Up Tables.

So, we have one hardware module for instruction C1 and another for C2.

Now, we can feed data D1 and D2 into these hardware modules simultaneously and perform both operations at the same time.

This means we're performing tasks much faster than a CPU or traditional processor could ever dream of.

But there's a crucial point to note here: this parallel processing magic only happens when the execution of one operation, say C2, doesn't depend on the result of another, like C1.

In other words, these operations need to be independent of each other, just like what's shown in the figure.

It's also important to understand that this isn't limited to small operations; you can parallelize huge algorithms too.

Imagine partitioning an extensive algorithm into distinct processing units within an FPGA and executing them concurrently.

This approach dramatically ramps up processing speed and the execution of operations. It's all about making the most of FPGA's parallel processing power.

Now, let's explore the second reason behind why FPGAs outpace processors in speed.

Customization

You see when you work with processors, you're stuck with a fixed architecture and hardware.

But here's where FPGAs shine. They give you the power to implement custom-tailored hardware for each specific task.

Let me illustrate this with an example.

Say you need to implement a digital filter. If you were to tackle this with a processor, you'd have to write code that follows the formula for that filter using the processor's built-in instructions.

Now, shift gears to the FPGA realm.

Here, you can roll up your sleeves and design brand-new, highly optimized hardware dedicated solely to the job of implementing that particular filter.

This level of customization enables you to implement hardware with the potential for much higher processing speeds.

So, it's these two critical factors that help clarify why FPGAs leave processors in the dust when it comes to speed.

It's all about the parallel processing capability and the ability to tailor hardware to the task at hand.