Key differences between hardware and software RNG and why should you care

Let’s play a game. Tell us a random number between 1 and 100. Fifty-four? Nice! But how did you choose this particular one? Perhaps it has some significance for you? Perhaps you’ve heard it somewhere today and it surfaced from your subconscious? Or maybe it was truly random. But how can we be sure that’s the case? Without trying to sound like a Vice video intro, our point is – a hundred percent random is not possible in theory. In a deterministic universe, nothing can be truly random. But we can get close enough. Read on.

Why is Randomness Even Important?

In a world of patterns, you would be surprised how important randomness is. Not just important, but often crucial. And that’s especially true for the world of computing. Randomness is an essential component for many applications and systems. It allows for unbiased selection, unpredictable results, and secure encryption. Translated to the language of mortals, we use randomness for the generation of unique IDs, ensuring fair gaming, and keeping our data safe from hackers, in statistical studies and scientific simulations among others. And while it’s easy for you to write down ten random numbers without breaking a sweat, instruct your computer to do something similar without special software and you will get a blue screen of death. Well, not literally. We’re just being dramatic. But for that purpose, humans had to come up with something special. Actually, two special things. An algorithm, and a product that helps computers do random things. And it’s pretty awesome. The technology behind it is called Random Number Generation (RNG).

Hardware RNG vs Software RNG

If you think atomic clocks are awesome, wait until you see how hardware RNGs work. They use microscopic physical phenomena such as thermal noise or radioactive decay to generate random numbers. How amazing is that? These devices usually come in the form of external USB sticks or built-in components. They are considered more secure than software RNGs since they rely on unpredictable natural processes rather than mathematical formulas. And while you may think such a device would cost thousands of dollars, you can grab an RNG USB stick from Amazon for about 100 USD. Of course, there are different tiers of these devices, and more capable ones indeed cost thousands of dollars.

On the other hand, software RNGs use mathematical algorithms to generate pseudo-random numbers. The technology was drafted more than 70 years ago when John von Neumann created the first pseudorandom generator in 1946. Today we have several different variations that work by similar principles. Usually, these algorithms take a seed value, perform mathematical operations on it, and output a sequence of numbers that seem random. This is why software RNGs are not considered truly random but are sufficient to be used securely and fairly in most applications. Compared to hardware RNGs, they are generally much faster, more flexible, and more cost-effective.

One major advantage of hardware RNG is its ability to generate truly random numbers without relying on any predetermined algorithm. This makes it nearly impossible for hackers to predict or manipulate the output. Additionally, since hardware RNG does not rely on outside sources such as network connections or system time, it can be used in offline and isolated systems. However, hardware RNGs are not without their flaws.

To answer the real questions – hardware RNGs are “more random” out of the two.

Importance of Knowing the Difference

Now, you may be thinking – “Who cares? As long as I get a random number, it doesn’t matter if it’s hardware or software generated.” And while that may be true when you’re picking a number for your next lotto ticket, it’s a different story for systems that require high levels of security. There are applications where knowing the difference is crucial. For example, in online gambling platforms like Spin Fever Casino, using good software RNG ensures fair gameplay and prevents the casino from manipulating the outcomes. It doesn’t really matter that this algorithm is not theoretically 100% random since the outcome for the user is the same as if it were. 

Similarly, in cryptography and security systems, you should be using a hardware RNG which provides an extra level of protection against potential attacks. In scientific simulations and statistical studies, using a hardware RNG ensures unbiased results. And in cases where true randomness is not that important, a software RNG fills the gap.

The Future of Randomness

The need for secure and truly random numbers will never go away. If anything, we will need better RNG technology in the future if we want to keep up with ever-evolving computer systems that ask for better security. This has led to research in quantum random number generators which would use the principles of quantum mechanics to produce unpredictable and truly random numbers. In theory, quantum computers could be used as true random number generators, as their operation relies on fundamental quantum phenomena. However, this technology is still in its early stages and has a long way to go before it can be used for practical applications. Until that happens, hardware and software RNGs continue to serve their purposes.

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