Example Models for TPU
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Some models that can benefit significantly from TPUs include: Large language models: Models like BERT, GPT-3, and Transformer-XL have billions of parameters and do massive amounts of low-precision computation. TPUs can train these models much faster than GPUs due to their low-precision performance and scalability. Google's BERT model was originally trained on TPUs. Recommendation systems: Recommendation models also tend to be very large, with many embeddings and weights to optimize. The fast matrix multiplications and throughput of TPUs are well suited for these types of models. Many companies use TPUs to train their production recommendation systems. Generative networks: Models like WaveNet, PixelCNN, and StyleGAN perform a huge number of operations to generate realistic images, audio, text, etc. These types of unsupervised learning tasks benefit greatly from the low-precision performance and efficiency of TPUs. Reinforcement learning: RL models have to interact with environments by performing many trial-and-error operations. This requires fast low-precision computation and experimentation, which TPUs can provide better than GPUs. Google's DeepMind trained AlphaGo on TPUs. Highly quantized neural networks: Some models use extremely low precision (e.g. 1-4 bit) activations and weights to achieve huge performance gains. TPUs specialized dot-product units can perform 4-bit and 8-bit quantized matrix multiplications up to 30x faster than GPUs. This enables fast training of highly quantized models that would otherwise be impractical. In general, any model that can benefit from high throughput low-precision computation, fast matrix multiplications, and scalability is a good candidate for TPUs. This includes many large neural networks, especially in the fields of NLP, computer vision, reinforcement learning, and collaborative filtering. While GPUs still have strengths for some models that require high single-precision performance or more compute per chip, TPUs have enabled huge advances in model size and speed for many ML applications. Let me know if you have any other questions! |