So what is this “quantization” thing anyway? Well, it’s basically the process of converting floating-point numbers (which are used by most deep learning models) into fixed-point integers. And why would we want to do that? Because it can significantly reduce the size and computational cost of our language models!
But let’s not get too technical here, alright? Instead, let’s focus on some practical examples. For instance, have you ever heard of BERT (Bidirectional Encoder Representations from Transformers)? It’s a state-of-the-art language model that can understand the context and meaning behind words in a sentence. And guess what? Researchers at Google recently quantized BERT using a technique called post-training quantization, which resulted in a 35% reduction in size without sacrificing accuracy!
Now, you might be wondering how does this work exactly? Well, the process involves converting all of the floating-point weights and activations in our language model into fixed-point integers. And here’s where things get really interesting: instead of using a traditional quantization scheme (which would involve rounding or truncating our numbers), we can use something called “symmetric quantization”!
This technique involves dividing the input range into 256 equally spaced intervals, and then assigning each interval to one of 8 bits. And here’s where things get really cool: by using a special activation function (called ReLU), we can actually represent any number within that interval as a single bit!
So what does this mean for us? Well, it means that our language models can now be trained and run on much smaller devices like smartphones or embedded systems. And best of all, they’ll still perform just as well (if not better) than their floating-point counterparts!
But don’t take our word for it check out some of the research papers that have been published in this area! For instance, there’s a paper called “Quantization and Pruning for Deep Neural Networks” by Hubara et al., which shows how you can reduce the size of your language model by up to 90% without sacrificing accuracy. And then there’s another paper called “Deep Compression: Training Deep Neural Nets with Pruning, Weight Sharing and Quantization,” which demonstrates how you can achieve a 35x reduction in size while still maintaining high levels of performance!
It might sound like a bunch of technical jargon at first, but trust us: this is the future of AI! And who knows? Maybe one day we’ll be able to fit an entire BERT model onto a single chip!
Until then, keep learning and exploring because that’s what language nerds do best.