In this post, we delve into the architecture of Google’s First Tensor Processing Unit (TPU v1) and its performance objectives. The TPU v1 project aimed to develop an ASIC that would provide a 10x cost-performance advantage on inference compared to GPUs, all while being cost-effective and scalable. The TPU v1 utilized a systolic array approach for matrix multiplications and a CISC design with a minimal instruction set for efficiency. The TPU v1 excelled in energy efficiency and speed for inference tasks, outperforming GPUs significantly. While the TPU v1 was designed specifically for inference, its success paved the way for further advancements in the field.
https://thechipletter.substack.com/p/googles-first-tpu-architecture