Central processing units (CPUs), graphics processing units (GPUs), and data processing units (DPUs) are all common in today’s computing landscape. GPUs, in particular, have gained prominence with the rise of artificial intelligence (AI).
You may have heard of tensor processing units (TPUs), a creation by Google only accessible through their cloud services.
But what exactly are TPUs, and why might you require them?
In essence, TPUs are specialized processing units designed for handling the high-dimensional data present in AI processing tasks. Before delving into their specifics, let’s compare them to other types of processors.
CPUs, the fundamental component of computing, are known for their versatility. With multiple cores handling various computing functions simultaneously, CPUs excel at managing instructions and coordinating system activities.
However, the need to offload intensive processing tasks to specialized chips has existed alongside CPUs since their inception.
What use are TPUs when GPUs exist?
Enter GPUs, originally designed for graphics processing in gaming but later repurposed for AI tasks due to their proficiency in matrix operations. These operations, involving matrices and multiple dimensions, align well with AI requirements.
While GPUs can handle matrix operations, TPUs are custom-built for this purpose, offering distinct advantages.
Nvidia has emerged as a key player in the GPU market for AI applications, though alternatives are available from various vendors.
What do DPUs do?
DPUs, found in servers, specialize in data transfer, reduction, security, and analytics. By offloading these tasks from CPUs, DPUs enhance system performance by allowing CPUs to focus on general orchestration responsibilities.
Intel, Nvidia, Marvell, AMD, and cloud providers like Amazon Web Services offer DPUs, with options such as Nitro cards.
What’s special about TPUs?
Introduced around 2016, TPUs are tensor processing units designed for handling high-dimensional data crucial in AI operations. These units are built around ASIC chips, specifically matrix-multiply units (MXUs), tailored for high-dimensional calculations.
In contrast to CPUs, which are general-purpose processors, TPUs stand out for their specialized design. While DPUs and GPUs can also utilize ASICs or FPGAs, TPUs are purpose-built for high-dimensional number calculations.
Google’s TPUs, optimized for its TensorFlow AI framework, deliver high-performance computing capabilities for advanced AI models. Supporting frameworks like PyTorch and Jax, Google TPUs enable tasks such as image classification, language model operations, and more.
While TPUs offer specialization, building in-house AI systems may not necessitate them. GPUs can often suffice, and the performance advantage of TPUs remains debatable. However, for cloud-based AI projects, TPUs seamlessly integrate with Google’s AI software stack.
