First: what are these fancy Transformers anyways? They’re basically neural networks that can handle long sequences of data (like text or speech) without losing accuracy over time. But here’s the catch they require a ton of memory to run, which can be a problem for big datasets and complex models.

So how do we fix this issue? Well, there are two main techniques: locality-sensitive hashing (LSH) and reversible residual layers. Let me explain each one in more detail:

1. Locality-Sensitive Hashing (LSH): This is a fancy way of saying that instead of using dot product attention (which can be slow for long sequences), we’re going to use LSH to speed things up. The idea here is to replace the expensive dot products with faster hash functions, which can help reduce memory consumption and improve performance.

For example, let’s say you have a sequence of words that looks like this: “the quick brown fox jumps over the lazy dog”. If we use LSH instead of dot product attention, we can quickly find all the words that are similar to “quick” without having to compute their exact distances. This can be really useful for tasks like text classification or machine translation, where you need to process large amounts of data in real-time.

2. Reversible Residual Layers: Another way to reduce memory consumption is by using reversible residual layers instead of standard residuals. The idea here is that we can store the activations only once (instead of multiple times) during training, which can help save a lot of memory and improve performance.

For example, let’s say you have a model with 10 layers. If you use reversible residual layers instead of standard residuals, you can reduce your memory consumption by up to 90%! This can be really useful for tasks like speech recognition or image processing, where you need to process large amounts of data in real-time without sacrificing accuracy.

If you want more details (or if you just like reading nerdy stuff), check out the paper by Nikita Kitaev et al. that I mentioned earlier. And as always, feel free to reach out if you have any questions or comments!