Google TPU V4 : Specifications, Architecture, Working, Differences & Its Applications

Tensor Processing Units by Google are ASICs designed to speed up machine learning workloads with TensorFlow software. Generally, TPUs can perform matrix operations fast, which makes them perfect for machine learning workloads. TPU was designed in such a way that it has the capacity to handle the high computational requirements of AI (artificial intelligence) and ML (machine learning). Thus, there are different generations of TPUs available, from the first generation to the seventh generation. This article elaborates on fourth-generation TPU, like Google TPU V4, working and its applications.

What is Google TPU V4?

The Google TPU V4 is a fourth-generation specialized hardware accelerator that is designed for ML workloads, mainly for large language models. This TPU is well known for its energy efficiency, scalability, and performance. It is the successor to the TPU v3, which offers energy efficiency enhancements and significant performance. Its large, high-speed, and reconfigurable interconnect network enables efficient communication between hundreds of chips within a pod supercomputer.

Specifications

The specifications of Google TPU V4 include the following.

• The TPU v4 uses a custom-designed architecture of Google adapted for ML workloads and optimized for low latency and high throughput.
• It delivers up to 275 TFLOPS of performance for 16-bit floating point precision.
• It is a significant boost over the earlier TPU v3, and it provides approximately 100 teraflops.
• Every TPU v4 chip includes 100 GB of HBM, which provides much more memory bandwidth compared to its earlier processor.
• TPUs are connected through a high-speed interconnect, which allows scaling across various devices.
• The TPU v4 Pods deliver up to 1 exaflop of processing power when completely scaled.
• It is built with a high-speed interconnect fabric, which allows very efficient communication between two TPUs by allowing distributed ML workloads.
• It is power-efficient with higher performance for each watt.
• This chip is incorporated into ML services and Google Cloud’s AI.
• It is optimized for distributed training and model parallelism, which makes it perfect for scaling models that extend across various devices.
• It is greatly optimized for own TensorFlow framework of Google however, it also supports other trendy frameworks like JAX and PyTorch through compatibility layers.

Google TPU V4 Architecture

The Google TPU v4 architecture consists of several key components that work mutually to deliver higher performance for ML workloads. These components have specialized units for high-bandwidth memory, matrix multiplication, cooling systems, and optical interconnects.

The main feature of this architecture is a 3D torus interconnection network, which interconnects separate TPU chips within a 3D configuration by allowing efficient data transfer and communication between chips in a large-scale TPU pod. This feature is allowed by OCSes or optical circuit switches, which energetically reconfigure the interconnect to optimize scalability, resource utilization, and performance for various ML models & workloads.

Google TPU V4 Architecture Components

Google TPU V4 Architecture includes different components, which are explained below.

TensorCores

The TensorCores play a key role in TPU, specially optimized for tensor operations and matrix multiplication. Every TPU v4 chip includes two TensorCores, where every core has four 128×128 MXUs (matrix multiply units). So these units handle the core ML operations like those utilized in deep learning models. In addition, the TensorCore allows different operations like integer operations and bfloat16 matrix multiplication, which are extremely optimized for ML tasks like training & inference.

High-Bandwidth Memory or HBM

Each TPU v4 chip is set with 32 GB of HBM. So this memory system provides ~1200 GB/s, a very high memory bandwidth. It is fundamental for rapidly moving large datasets between the memory and the processor, particularly for training large models. This memory is integrated closely with the compute units, decreasing bottlenecks by ensuring that data can be efficiently processed.

SparseCore

It is a specialized accelerator incorporated into TPU v4 to process sparse matrix operations, which are common in modern deep learning models, especially for embeddings. This specialized accelerator allows the tensor processing unit to process embedding-heavy models very efficiently. This is perfect for models that use sparse matrix operations, like NLP models through large embedding layers.

Optical Circuit Switches or OCS

TPU v4 establishes a dynamic optical interconnect between optical circuit switches. The main function of these switches is to allow high bandwidth and flexible chip-to-chip communication. So the optical links produce a 3D torus network and an extremely scalable architecture that dynamically routes information between chips. These switches provide superior reliability and scalability as compared to usual copper-based interconnects like Infiniband. In addition, they decrease the overall system cost and power consumption.

Cooling System

TPU v4 uses liquid cooling systems for every board to handle the thermal loads and high power produced by the chips. This is essential for maintaining the best thermal conditions, mainly in large-scale data centers. Thus, this approach is more energy-efficient than usual air cooling methods, which improves the longevity and performance of the hardware.

Chip Design and Fabrication

TPU v4 is fabricated with a 7nm process, which allows lower power consumption and high transistor density as compared to earlier generations. So the die size of this processor is approximately 400mm², with the SparseCore, memory, TensorCores, and various components. In addition, its compact design helps in enhancing overall efficiency. TPU v4 chips’ clock speed is 1.05 GHz, which is optimized for both power consumption and performance.

Networking and Scalability

The optical interconnects allow TPU v4 to scale efficiently at the pod level. Thus, a single TPU v4 pod consists of 4096 chips, with a total compute capability of 1.1 exaflops.

Bisection Bandwidth

It is an interconnection that delivers 24 TB/s of bisection bandwidth by ensuring the communication between chips is sufficient to handle large-scale distributed workloads.

Scaling

This architecture allows dynamic scaling, including 64 racks arranged within a 3D torus network to make it very flexible & scalable for various workload sizes.

Pod Architecture

A TPU v4 pod is designed with 4096 chips, which are dispersed across 64 racks. Thus, this form factor is mainly designed for severe scalability. The 3D torus interconnect ensures that every TPU can efficiently communicate with others, decreasing bottlenecks when the pod scales up. So a full TPU v4 pod offers up to 1.1 exaflops of peak compute, which supports huge parallelism & high-throughput machine learning workloads.

Power and Efficiency

Each TPU v4 chip uses 170 to 192 Watts in full load. It provides 2.7 × better performance for each watt as compared to TPU v3. Thus, it makes it a very energy-efficient solution, mainly for ML workloads. This TPU architecture can be designed with sustainability in mind, which delivers up to 20× lower CO₂ emissions as compared to related ML accelerators.

Google TPU v4 Software

The Google TPU v4 architecture uses a sophisticated software stack to allow its high-performance ML capabilities. Thus, this software stack is designed to exploit the competence of the underlying hardware for NN workloads. Basically, the TPU v4 software provides a strong & flexible environment that allows researchers and developers to train and organize large-scale ML models efficiently by using the particular hardware abilities of the TPU.

How does Google TPU V4 Work?

The Google TPU V4 is a specialized hardware accelerator that works by performing tensor computations very efficiently as compared to general-purpose GPUs or CPUs. So these are exclusively optimized for ML tasks like deep learning. The step-by-step working of Google TPU V4 is discussed below.

First input data in the multi-dimensional arrays form is fed into the tensor processing units. After that, the TPU V4 achieves matrix multiplications, tensor operations, and convolutions with its tensor cores. In model training, gradients can be measured with backpropagation, and weights are iteratively updated.

After model training is done, this TPU is utilized to run the model and produce predictions on fresh data. Large models (or) datasets can be processed across several TPUs within a TPU pod, which allows efficient parallel computation. In addition, TPUs in cloud integration can be accessed & scaled through Google Cloud by allowing businesses and researchers to use the authority of TPUs without handling physical hardware.

Thus, the TPU V4 is a very efficient and powerful tool where its design is focused on high-bandwidth memory, specialized cores, and tensor computation. In addition, it makes it the best choice for large-scale AI tasks, particularly those that involve difficult neural networks & big datasets.

Difference between Google TPU V4 and Google TPU V5

The difference between Google TPU V4 and Google TPU V5 includes the following.

Advantages

The advantages of Google TPU V4 include the following.

• It has massive computational power up to 260 teraflops.
• Power efficiency is higher for each watt.
• Seamlessly scale from separate TPUs to TPU Pods.
• It is optimized for ML frameworks like PyTorch and TensorFlow.
• In addition, it has high-bandwidth memory for quick data processing.
• Quicker training times & decreased latency for inference.
• It is cost-effective mainly for large-scale AI projects.
• This TPU is particularly for tensor operations to make it perfect for deep learning.
• It supports distributed computation and large-scale models.
• It has superior cooling & reliability to keep performance.
• It allows cutting-edge research within AI.
• This TPU has safety features for data protection and privacy.
• It has global availability within Google Cloud for simple access & scalability.

Disadvantages

The disadvantages of Google TPU V4 include the following.

• It has limited framework support, mainly outside of TensorFlow.
• It needs a Google Cloud account.
• These are expensive, particularly for personal use or small projects.
• In addition, it has limited customization as compared to general-purpose hardware or GPUs.
• It has a complex migration from GPU or CPU to TPU for accessible models.
• Vendor lock-in through the Google Cloud ecosystem & pricing.
• Regional accessibility limitations & potential resource queues.
• Overkill for small tasks or models with small batch sizes.
• It has precision loss because of mixed precision, which can be a problem for certain applications.
• It is not suitable for all AI workloads, particularly low-latency inference or non-tensor-based tasks.

Applications

The applications of Google TPU V4 include the following.

• The Google TPU V4 is designed for high-performance ML tasks. Thus, it can also be applied across a broad range of domains where fast tensor processing and large-scale computation are essential.
• The TPU V4 is perfect for training DNNs & large models like CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks) & Transformers.
• In addition, it can be used to train transformer-based models like T5, GPT & BERT, which need significant computational power because of the large number of parameters & huge datasets involved.
• The TPU handles high-throughput matrix multiplications, which are helpful in training models on wide text corpora.
• TPUs are brilliant at handling various tasks wherever convolution operations dominate, like image segmentation, image classification, and object detection.
• It is used in Generative Adversarial Networks & Variational Autoencoders, which need heavy computations to produce high-quality images, video, or even audio content.
• In addition, it is used to train agents in complex environments like robotics and games, which involve many simulations where each needs significant computing.
• It is used in autonomous vehicles for processing a large quantity of sensor data, which needs significant computing power for different tasks like scene understanding and object detection.
• It is used in the AlphaFold deep learning model, which predicts the protein’s structure depending on its amino acid sequences.
• TPUs can be used for scam detection in economic transactions, wherever deep learning models examine patterns & anomalies within huge datasets.
• The TPU V4 can speed up the training of AI-Generated Art & Music models that produce new paintings, music, and images based on learned patterns.
• In addition, these TPUs can be used to speed up AI models on edge devices like wearables, IoT devices, and smartphones, wherever fast processing and low-latency are significant.

FAQs

1). Can TPU v4 be used with PyTorch?

Yes. While TPU v4 is optimized for TensorFlow, it supports other frameworks like PyTorch and JAX through compatibility layers.

2). What memory does Google TPU v4 use?

TPU v4 uses High-Bandwidth Memory (HBM), thus providing up to 32 GB per chip and ~1.2 TB/s bandwidth for faster data processing.

3). How many chips are in a TPU v4 pod?

A single TPU v4 pod includes 4,096 chips arranged in a 3D torus network, delivering over 1 exaflop of compute power.

4). What is the power consumption of TPU v4?

Each TPU v4 chip consumes about 170 to 192 watts under full load, offering 2.7× better performance per watt compared to TPU v3.

5). Why does TPU v4 use optical interconnects?

Optical Circuit Switches (OCS) enable high-speed, energy-efficient communication between chips, improving scalability and reducing latency.

6). What is the fabrication technology of TPU v4?

Google TPU v4 is built using a 7nm process, allowing higher transistor density and improved power efficiency.

7). How does TPU v4 improve sustainability?

TPU v4 delivers up to 20× lower CO₂ emissions compared to similar accelerators, making it an energy-efficient solution for AI workloads.

8). Can individuals buy Google TPU v4 hardware?

No. TPU v4 is available only through Google Cloud services and cannot be purchased as standalone hardware.

9). What type of AI models benefit most from TPU v4?

Transformer-based models like GPT, BERT, and T5, as well as CNNs and RNNs for vision and NLP tasks, benefit the most due to TPU v4’s tensor-optimized architecture.

Conclusion:

 The Google TPU v4 is a major evolution with custom AI hardware from Google, striking a strong balance between scalability, energy efficiency, and performance. So it was the base for training a lot of large-scale ML models at its peak. The key highlights of this TPU v4 specialized hardware accelerator mainly include: high performance, massive scale, energy efficiency, advanced interconnect, and broad adoption. Here is a question for you: What is Google TPU V5?