Editing Machine learning (section)

=== Tensor Processing Units (TPUs) ===
[[Tensor Processing Unit|Tensor Processing Units (TPUs)]] are specialised hardware accelerators developed by [[Google]] specifically for machine learning workloads. Unlike general-purpose [[Graphics processing unit|GPUs]] and [[Field-programmable gate array|FPGAs]], TPUs are optimised for tensor computations, making them particularly efficient for deep learning tasks such as training and inference. They are widely used in Google Cloud AI services and large-scale machine learning models like Google's DeepMind AlphaFold and large language models. TPUs leverage matrix multiplication units and high-bandwidth memory to accelerate computations while maintaining energy efficiency.<ref>{{Cite book |last1=Jouppi |first1=Norman P. |last2=Young |first2=Cliff |last3=Patil |first3=Nishant |last4=Patterson |first4=David |last5=Agrawal |first5=Gaurav |last6=Bajwa |first6=Raminder |last7=Bates |first7=Sarah |last8=Bhatia |first8=Suresh |last9=Boden |first9=Nan |last10=Borchers |first10=Al |last11=Boyle |first11=Rick |last12=Cantin |first12=Pierre-luc |last13=Chao |first13=Clifford |last14=Clark |first14=Chris |last15=Coriell |first15=Jeremy |chapter=In-Datacenter Performance Analysis of a Tensor Processing Unit |date=24 June 2017 |title=Proceedings of the 44th Annual International Symposium on Computer Architecture |chapter-url=https://dl.acm.org/doi/10.1145/3079856.3080246 |series=ISCA '17 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=1–12 |doi=10.1145/3079856.3080246 |isbn=978-1-4503-4892-8|arxiv=1704.04760 }}</ref> Since their introduction in 2016, TPUs have become a key component of AI infrastructure, especially in cloud-based environments.