The NEW Chip Inside Your Phone! (NPUs)

Techquickie
16 Apr 202405:30

TLDRAI chips are becoming a significant selling point for smartphones, despite the devices' power consumption and heat generation limitations. Neural processing units (NPUs) are specialized components optimized for AI tasks, similar to how GPUs are better for graphics rendering than general-purpose CPUs. These NPUs are designed to run AI-based tasks efficiently without consuming excessive power. The push for integrating NPUs into phones is driven by the latency advantage and privacy benefits of running tasks like voice and facial recognition locally rather than in the cloud. While more advanced AI tasks like generative AI may not be feasible on current smartphones, features like Google's Magic Editor require internet connectivity due to the computational demands. Tech companies are still exploring the optimal balance between on-device and cloud-based AI tasks. As AI technology matures, it's expected that more AI functions will be run locally on devices, with manufacturers like AMD and Intel including NPUs in their consumer processors for enhanced capabilities.

Takeaways

  • 📱 **Neural Processing Units (NPUs)** are specialized chips in smartphones that are optimized for AI tasks but not as efficient for general computing tasks.
  • 🔥 NPUs are designed to run AI tasks without consuming too much power, similar to how GPUs are optimized for graphics rendering.
  • 🌐 There's a push for NPUs in phones due to the latency advantage of running AI tasks locally rather than relying on cloud-based AI, which can be slower due to data transmission times.
  • 🔒 Using NPUs can also enhance privacy by keeping data processing on the device instead of sending it to the cloud.
  • 🚫 Not all AI tasks are suitable for NPUs; more complex tasks like generative AI may still require cloud processing power.
  • 📈 Tech companies are still exploring the best use cases for on-device AI and figuring out the balance between on-device and cloud processing.
  • 💻 Companies like AMD and Intel are including NPUs in their consumer processors, indicating a growing trend towards integrating AI capabilities into personal computing devices.
  • 🎨 Features like Google's Magic Editor rely on cloud servers due to the high generative AI demands, which are not yet efficiently run on current NPUs.
  • 📊 The size of NPUs in phones is still relatively small as manufacturers are cautious about dedicating more hardware to AI before identifying clear use cases.
  • 🤝 Partnerships between hardware manufacturers and software developers are being formed to create applications that can leverage the capabilities of NPUs.
  • ⏱️ As technology progresses, it's expected that more AI functions will be run locally on devices, enhancing their 'brain power' for various applications.

Q & A

  • What are neural processing units (NPUs) and how do they differ from a phone's main CPU cores?

    -Neural processing units (NPUs) are specialized components in smartphones that are optimized for AI tasks. Unlike the main CPU cores, NPUs are designed to handle machine learning-based tasks efficiently without consuming excessive power. They are similar to GPUs in that they are better at parallel processing for specific tasks but are not suited for general-purpose computing.

  • Why is there a push to include NPUs in smartphones?

    -The push for NPUs in smartphones is driven by the desire to perform AI tasks locally on the device. This can reduce latency, improve user experience by providing faster responses, and enhance privacy by keeping data on the device rather than sending it to the cloud.

  • How do NPUs help with privacy concerns?

    -NPUs help with privacy by allowing data processing to occur on the device itself, reducing the need to send personal information to the cloud. This keeps sensitive data, such as voice recordings or facial recognition data, more secure.

  • What are the limitations of running advanced forms of generative AI on a phone?

    -Advanced forms of generative AI, such as those used for creating new media or generating stories, require significant computational power. Current NPUs in phones are not powerful enough to run these models efficiently due to their size and the power consumption limitations of mobile devices.

  • Why do some AI features, like Google's Magic Editor, require an internet connection?

    -Features like Google's Magic Editor require an internet connection because they use more advanced generative AI that is too demanding to run on the phone's local hardware. The phone relies on cloud servers to process the AI tasks and return the results in a reasonable amount of time.

  • How are tech companies approaching the monetization of AI as a service?

    -Many tech companies are still exploring how to monetize AI as a service. They often release features first, observe how they are used, and then integrate them into their business models at a later stage.

  • What is the current trend in hardware manufacturing regarding NPUs?

    -Hardware manufacturers are including NPUs in their consumer products with the intention of enabling AI features. They are cautious about dedicating too much hardware to AI until they have a clearer understanding of the specific use cases and requirements.

  • How are AMD and Intel incorporating NPUs into their consumer processors?

    -AMD and Intel are both releasing consumer processors with integrated NPUs. The goal is to enable features like Windows Studio Effects to run on the device, enhancing user experiences, such as improving the quality of video calls.

  • What is the future outlook for AI functions in consumer devices?

    -The future outlook indicates that consumer devices, both PCs and smartphones, will increasingly incorporate more AI functions. Manufacturers are aiming to run more AI tasks locally on devices, and there is a push for software development to take advantage of these NPUs.

  • What is the role of latency in deciding whether to use a phone's NPU or to offload tasks to the cloud?

    -Latency plays a significant role in this decision. If an AI task can be performed quickly on the device using the NPU, it can provide a better user experience by avoiding the wait time associated with sending data to the cloud, processing it, and receiving a response.

  • What are some common smartphone AI features that can benefit from running on device?

    -Common smartphone AI features that can benefit from running on device include voice recognition, facial recognition, and certain types of image correction. These tasks can be performed efficiently on the device with smaller AI models.

  • What is the 'sweet spot' that tech companies are trying to find regarding on-device versus cloud-based AI tasks?

    -The 'sweet spot' refers to the optimal balance between tasks that are best performed on the device using the NPU and those that are better offloaded to the cloud. This balance is determined by factors such as the complexity of the AI model, power consumption, speed of execution, and user privacy considerations.

Outlines

00:00

📱 AI Chips in Smartphones: Power and Efficiency

The first paragraph discusses how AI chips have become a significant selling point for smartphones, despite the devices' power consumption and heat generation limitations. It explains that neural processing units (NPUs) are optimized for AI tasks, similar to how GPUs are better for graphics rendering than general-purpose CPUs. The paragraph also touches on the latency benefits and privacy advantages of running AI tasks on device, as opposed to relying on cloud-based AI. It raises the question of why there's a push for AI chips in phones when cloud AI is an option, and notes that smaller AI models can be run locally on smartphones. The sponsorship message for the MSI mag 1250g PCI5 power supply is also included, highlighting its efficiency and modular design.

05:00

🚀 The Future of AI on Devices

The second paragraph explores the future of AI on consumer devices, noting that while more advanced forms of generative AI may not yet be efficiently run on phones, less demanding features like live translation can be executed on device. It mentions that companies are still determining the ideal balance between on-device and cloud-based AI tasks. The paragraph also discusses the monetization challenges for AI as a service and how hardware manufacturers are cautious about dedicating more hardware to AI until use cases are clearer. It concludes by observing the trend of manufacturers aiming for more local AI functions and the collaboration with software developers to utilize NPUs effectively.

Mindmap

Keywords

💡AI chips

AI chips, also known as neural processing units (NPUs), are specialized components within smartphones designed to efficiently perform AI-related tasks. They are optimized for machine learning algorithms and can run AI applications with lower power consumption compared to the phone's main CPU. In the video, AI chips are highlighted as a selling point for modern phones, emphasizing their role in handling tasks like voice and facial recognition without relying on cloud computing.

💡Neural processing units (NPUs)

NPUs are dedicated hardware within a device that accelerates AI computations. They are different from a phone's main CPU cores, being highly specialized for AI tasks, which makes them more energy-efficient for these specific operations. The video explains that NPUs allow smartphones to run AI models for features like voice recognition and image correction on-device, reducing latency and enhancing user experience.

💡Power consumption

Power consumption refers to the amount of energy used by electronic devices during operation. In the context of the video, it is a critical factor for smartphones when incorporating AI capabilities, as they have limitations on how much power they can consume without overheating or draining the battery quickly. NPUs help address this by being designed to perform AI tasks with minimal power usage.

💡Heat generation

Heat generation is the process by which electronic devices produce heat during operation, which can be a significant concern for smartphones with powerful components like AI chips. The video discusses how NPUs are designed to handle complex AI tasks without generating excessive heat, thus maintaining the device's performance and user safety.

💡Cloud AI

Cloud AI refers to the practice of running AI algorithms and models on powerful servers located remotely in data centers, rather than on the device itself. The video contrasts Cloud AI with on-device AI processing, discussing the advantages of local processing in terms of latency and privacy, despite the superior computational power available in the cloud.

💡Latency

Latency in the context of the video refers to the delay between a user's interaction with their device and the device's response, such as sending speech data to a server and waiting for the response. The video argues that having AI processing capabilities on the device can significantly reduce latency, providing a faster and more responsive user experience.

💡Privacy

Privacy, as discussed in the video, is a benefit of processing AI tasks on the device rather than in the cloud. By keeping data local, there's less risk of sensitive information being transmitted over the internet, which could potentially be intercepted or misused. This is particularly relevant for features like voice recognition, where users may prefer their speech data to be processed directly on their phone.

💡Generative AI

Generative AI is a type of artificial intelligence that can create new content, such as stories, images, or music. The video mentions that while generative AI is powerful, it is currently too complex to be efficiently run on a smartphone's NPU. Examples given include AI-generated stories from chatbots or AI art. These tasks typically require more computational power and are better suited for cloud servers.

💡Google's Magic Editor

Google's Magic Editor is a feature mentioned in the video that uses generative AI to edit images on Google Pixel phones. However, it requires an internet connection, indicating that the processing is done on cloud servers due to the intensive AI computations involved. This contrasts with less demanding features that can be executed on the device itself.

💡AI as a service

AI as a service refers to the delivery of AI functionalities over the internet, without the need for the user to have the computational resources on their device. The video discusses how tech companies are still exploring monetization strategies for AI services and how they are often rolled out before a clear business model is established.

💡Hardware manufacturers

Hardware manufacturers are companies that produce the physical components of electronic devices, such as smartphones and computers. The video talks about how these manufacturers are integrating NPUs into their products, with the aim of enabling more AI features on devices. They are cautious about dedicating too much hardware to AI until they understand the most beneficial use cases for consumers.

💡Windows Studio Effects

Windows Studio Effects is a feature mentioned in the video that uses AI to enhance the quality of video calls, such as through background blur or improved lighting. The feature is an example of how AI capabilities are being integrated into operating systems and applications to improve user experiences, and it's expected to run on device, leveraging the NPU for processing.

Highlights

AI chips are becoming a major selling point for smartphones.

Neural processing units (NPUs) are optimized for AI tasks but not as efficient for other tasks.

NPUs are similar to GPUs in their parallel processing capabilities.

A small die area dedicated to AI can run machine learning tasks with low power consumption.

There's a push for integrating NPUs into phones for faster, on-device processing.

Cloud AI is powerful but local processing on devices has the advantage of lower latency.

Running AI functions locally can improve privacy by keeping data on the device.

Smaller AI models suitable for smartphones can be run on-device for features like voice and facial recognition.

Google's Magic Editor relies on cloud servers due to the complexity of generative AI.

Tech companies are still exploring the optimal balance between on-device and cloud-based AI tasks.

AI as a service products are often rolled out before a clear monetization strategy is established.

The die areas of NPUs in phones are kept relatively small to allow for future adjustments based on use cases.

AMD and Intel are including NPUs in their consumer processors for enhanced AI capabilities.

Manufacturers are aiming to increase the number of AI functions running locally on devices.

Software developers are partnering with hardware manufacturers to create applications that utilize NPUs.

The future of gadgets is expected to include significantly more AI capabilities.

Viewer engagement is sought through likes, dislikes, comments, and subscriptions for the video content.

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