Well, we’ve got a solution for ya NN compression for analog storage devices!

Now, before you start rolling your eyes and thinking this is some kind of joke, let me explain. You see, traditional digital storage methods are great for storing data in binary form (0s and 1s), but they’re not so hot when it comes to compressing neural networks. That’s where analog storage devices come in they can store information using a range of voltages instead of just two states like digital storage does.

So, how do we go about compressing our beloved AI models for these fancy new devices? Well, first off, the benefits. By storing neural networks on analog devices, we can reduce their size by up to 10x! That means less space needed and more room for other important things like cat memes or your favorite TV shows.

But how do we actually compress these models? Well, there are a few different methods out there, but one popular technique is called quantization. Essentially, this involves reducing the number of bits used to represent each weight in the neural network. Instead of using 32-bit floating point numbers (which can take up quite a bit of space), we can use just 8 or even 4 bits!

Now, you might be thinking “But won’t that affect the accuracy of my model?” And to that I say not necessarily! In fact, some studies have shown that quantized models can actually perform better than their full-precision counterparts in certain situations. Plus, if you really need those extra bits for precision, you can always use a technique called post-training quantization (PTQ) which involves training the model with full precision and then converting it to lower bit widths after the fact.

It’s not just some crazy sci-fi concept anymore, And who knows? Maybe one day we’ll be able to store entire AI systems on a single grain of rice or something equally mind-blowing.