Tensor Processing Units (TPUs) represent a specialized class of application-specific integrated circuits (ASICs) conceived by Google with a dedicated focus on enhancing the efficiency of neural network machine learning tasks, all within the framework of Google's TensorFlow software. The strategic design of TPUs centers around optimizing the acceleration of both the training and inference processes of machine learning models. This specialized approach renders TPUs exceptionally well-suited for a diverse range of applications spanning natural language processing, computer vision, and speech recognition.
A core distinction between TPUs and conventional processors such as central processing units (CPUs) and graphics processing units (GPUs) lies in TPUs' specialization for the matrix multiplication operations that are pervasive within neural networks. This architectural specialization grants TPUs the ability to significantly outpace general-purpose processors in both training and inference tasks, effectively circumventing the processing bottlenecks that can impede machine learning workflows.
The journey of TPUs began in 2015 when Google initially deployed them for internal usage. By 2018, they extended availability to third parties by incorporating TPUs into their cloud infrastructure, as well as offering a scaled-down version of the chip for purchase.
Google Cloud Platform provides managed services for Cloud TPUs, empowering developers to undertake large-scale training and deployment of machine learning models. On the other hand, Edge TPUs, a smaller and more power-efficient variant of the original TPU chip, are strategically designed for integration into edge devices like smartphones and Internet of Things (IoT) devices.
TPUs have already left an indelible mark on the landscape of machine learning achievements. They have underpinned pivotal milestones such as the creation of the AlphaGo program that secured victory over a professional Go player in 2016, the development of the BERT language model revered as one of the foremost natural language processing models globally, and the engineering of the Cloud Vision API which confers straightforward image recognition capabilities to applications.
Within the realm of technical specifications, TPUs distinguish themselves by their remarkable speed in matrix multiplication operations. A single TPU can execute up to a staggering 180 teraflops of floating-point operations per second, dwarfing high-end GPUs by nearly 100 times in terms of performance. Additionally, TPUs boast exceptional energy efficiency, consuming around 10 watts of power per teraflop—significantly less than the power consumption of GPUs.
Furthermore, TPUs are tailored for operation within clusters, fostering the scalability necessary for extensive training and deployment of machine learning models. This scalability is seamlessly facilitated through Google Cloud Platform's managed service offerings, alleviating the complexities associated with the underlying hardware and enabling developers to effortlessly harness the power of TPUs in their projects.
While TPUs are a nascent technology, they have already demonstrated the potential to reshape the landscape of machine learning. Their transformative impact is evident through their involvement in some of the most advanced machine learning applications worldwide. As the evolution of TPUs continues, their potency and ubiquity within the field are poised to grow even further.
Tensor Processing Units: History and hardware