Artificial Intelligence is the hottest thing in town. It has taken over everything and has permeated nearly every layer of society. When you are discussing AI, two terms tend to pop up and they are NPU and TPU. So in this article, we’re going to discuss NPU vs TPU: what they are and how they compare.

If you own a smartphone, you may have heard of terms like CPU cores, GPU, or SoC. These are some of the components that make your smartphone work. However, there is a lesser-known component on our smartphones that is specifically tailored for machine learning and artificial intelligence (AI). This component is called an NPU or a TPU. It helps your smartphone do things that normally require human intelligence, such as understanding language, recognizing images, solving problems, or learning from data. In other words, an NPU or TPU makes your phone smart.

Artificial Intelligence and Machine Learning (NPU vs TPU)

What is machine learning and AI?

Before we talk about NPUs and TPUs, let us first understand what machine learning and AI are. Machine learning and AI are fields of science and technology that aim to create computers or machines that can do things that normally require human intelligence. For example, machine learning and AI can help us with tasks such as:

  • Searching the web
  • Playing games
  • Self-driving cars
  • Diagnosing diseases etc.

Machine learning and AI can also create new things, such as music, art, or stories, by learning from existing examples, generating original content, and expressing creativity and emotions.

Neural Network vs Human Brain (NPU vs TPU)

Neural Networks

Think of machine learning and AI as a brain. This brain is made up of mathematical models that help it to learn from new information. One of the most common types of these models is called a neural network, which works a lot like our human brain.

These neural networks are made up of tiny parts called neurons which are connected by links. By changing the strength of these links, the network can learn from the information that it is given.

These networks can do a lot of different things. They can sort things into groups, predict outcomes, create new things, and even learn from rewards and punishments. What they do depends on how they’re built and what information they’re given.

The work of these networks is split into two parts:

  • training and
  • inference.

Training is when the network is given information to study and learn from. Inference is when the network is tested i.e. the network uses what it’s learned to make decisions about new information.

See: What is an NPU?

What is an NPU

What is an NPU?

An NPU, or Neural Processing Unit, is a specialized hardware AI accelerator. It is designed to perform the mathematical operations required for machine learning tasks, particularly those involving neural networks. NPUs speed up the training and inference phases of neural networks. This allows them to run more efficiently on many devices.

NPUs are similar to other hardware accelerators, such as GPUs and TPUs. However, they are specifically optimized for tasks related to artificial neural networks. They are typically used with CPU cores to provide additional processing power for machine learning tasks.

NPUs are developed in-house for use by different companies. These different NPUs have different names, designs, and features. Some of the most common NPUs are:

  • Qualcomm Hexagon DSP
  • Samsung Neural Processing Solution
  • Apple Neural Engine
  • Huawei Da Vinci Architecture
  • MediaTek APU

What is a TPU?

A TPU, or Tensor Processing Unit, is also a type of specialized hardware AI accelerator. It is designed to perform the mathematical operations required for deep learning tasks, especially those involving tensors.

 

A Tensor is a multidimensional array of numbers that represent the data and parameters of a neural network. Just like NPUs, TPUs speed up the training and inference phases of neural networks, allowing them to run more efficiently on the cloud or the edge.

TPUs are similar to other hardware accelerators, such as GPUs and NPUs, but they are specifically optimized for tasks related to deep learning. They are typically used with a CPU to provide additional processing power for machine learning tasks.

TPUs are developed by Google and are only available on the Google Cloud Platform or the Google Pixel phones. They have different versions and generations, such as TPU v1, TPU v2, TPU v3, and TPU v4. Some of the features of TPUs are:

  • Tensor Core: It is the main component of a TPU that performs matrix multiplications and convolutions. These are the core operations of deep learning. It can process large amounts of data in parallel and with high precision.
  • TensorFlow: It is the main software framework that is used to program and run deep learning models on TPUs. It is an open-source library that provides various tools and functions for building and deploying machine learning applications.
  • TensorBoard: It is a visualization tool that is used to monitor and debug deep-learning models on TPUs. It can display various metrics and graphs, such as accuracy, loss, gradients, weights, and activations.

NPU vs TPU

NPU vs TPU: A Comparison

Now that we have a basic understanding of what NPUs and TPUs are, let us compare them in terms of performance, features, advantages, disadvantages, and applications.

Performance

Both NPUs and TPUs are highly efficient and powerful resources for machine learning and AI. However, they may have different performance levels depending on the design and implementation of the hardware and software.

In general, TPUs may have a slight performance advantage over NPUs due to their specific optimization for deep learning tasks. However, the specific performance of an NPU or a TPU will depend on various factors, such as the type and size of the machine learning model, the amount and quality of the data, the configuration and optimization of the hardware and software, and the availability and cost of the resources.

Some of the performance metrics that can be used to compare NPUs and TPUs are:

  • Latency
  • Throughput (data handling ability)
  • Accuracy
  • Power (battery) consumption

Features

Both NPUs and TPUs have some common and unique features that affect their performance and functionality. Some of the features that can be used to compare NPUs and TPUs are:

Architecture

Architecture in machine learning refers to how the model’s hardware and software components are organized. It decides how data is handled and processed across the system. Various architectures have pros and cons like scalability, flexibility, compatibility, and reliability. For instance, NPUs offer diverse and flexible structures usable by any entity, supporting a broad array of algorithms. In contrast, TPUs, crafted by Google use fixed designs optimized for one task (deep learning).

Precision

Precision in machine learning is the number of bits used to represent data, impacting accuracy and efficiency. Higher precision offers accuracy but demands more memory and computation. Lower precision sacrifices accuracy for less memory and computation. NPUs provide variable precision like integers or floats. In contrast, TPUs maintain fixed precision, using a custom 16-bit floating-point format (bfloat16) ideal for deep learning.

Sparsity

Sparsity in machine learning refers to the proportion of zeros or insignificant values in both the data and parameters of a model. It impacts the model’s efficiency and performance. Higher sparsity indicates more structure and redundancy in the data, offering opportunities for compression and optimization. On the other hand, lower sparsity means less structure and more challenges for compression and optimization. NPUs often have more structured sparsity, grouping zero elements for efficient skipping, while TPUs may exhibit more unstructured sparsity, requiring compressed formats for storage and access due to zero elements being anywhere in the data.

NPU vs TPU: Advantages and Disadvantages

Both NPUs and TPUs have some advantages and disadvantages over other types of processors, such as CPUs and GPUs, for machine learning and AI tasks on smartphones. Some of the factors that can affect the pros and cons of using NPUs and TPUs are cost, availability, compatibility, scalability, and security. For example, you can consider the following points:

Cost

NPUs and TPUs can be expensive to develop and use, as they require specialized hardware and software components, as well as technical skills and knowledge. NPUs may be cheaper than TPUs, as they are integrated into the smartphone SoC, which means they do not need additional devices or services. TPUs may be more expensive than NPUs, as they are only available on the Google Cloud Platform or on the Google Pixel 6 and Pixel 6 Pro phones, which means they may require extra fees or subscriptions.

Cost: NPUs are cheaper

Availability

NPUs and TPUs are not widely available for all devices. This is because they are developed by specific companies or organizations, such as Google, Samsung, Apple, and Huawei.

With that being said, NPUs may be more available than TPUs. This is because they are integrated into many smartphones and they can support a broader range of machine learning algorithms. TPUs are less available than NPUs, as they are only available on the Google Cloud Platform or Google Pixel phones. They are specifically designed for deep learning tasks.

Availability: NPUs are more available

Compatibility

NPUs may be more compatible than TPUs, as they can use different types and sizes of data, such as integers, floats, or fixed-point numbers. TPUs may be less compatible than NPUs, as they use a custom 16-bit floating-point format called bfloat16, which may not be supported by all the frameworks and libraries.

Compatibility: NPUs

Scalability

NPUs and TPUs can be scalable, which means they can handle different amounts and types of data and computation, depending on the needs and demands of the machine learning and AI tasks. NPUs may be less scalable than TPUs, as they are limited by the smartphone’s battery life, storage capacity, and thermal management. TPUs may be more scalable than NPUs, as they can leverage cloud computing resources, such as Google’s data centers and servers, which can provide more power, storage, and flexibility.

Security

NPUs may be more secure than TPUs, as they can run the machine learning and AI tasks on the device itself, without relying on the cloud or the internet, which can reduce the risk of data leakage, hacking, or interception. TPUs may be less secure than NPUs, as they may depend on the internet connection, which can expose the data and the results to potential threats, such as malware, spyware, or phishing.

Conclusion

 NPUs and TPUs can perform complex mathematical operations faster and more efficiently than general-purpose processors, such as CPUs and GPUs, which are the main components of a smartphone SoC. NPUs and TPUs can also reduce power consumption and improve the battery life of a smartphone, as they can offload some of the work from the CPUs and GPUs.

We have also seen that NPUs and TPUs have some differences and similarities in terms of performance, features, advantages, disadvantages, and applications. In general, TPUs may have a slight performance advantage over NPUs due to their specific optimization for deep learning tasks. However, the specific performance of an NPU or a TPU will depend on various factors, such as the type and size of the machine learning model, the amount and quality of the data, the configuration and optimization of the hardware and software, and the availability and cost of the resources.

Machine learning and AI applications use both NPUs and TPUs, but different scenarios suit them best. NPUs, common in many smartphones, are ideal for on-device machine learning, and handling various models and tasks. TPUs, available on the Google Cloud Platform and certain Google Pixel models, are better for cloud-based machine learning, and managing large and complex tasks. Both can support edge AI, offering efficient machine learning capabilities on edge devices without needing the cloud or internet.

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