AI is everywhere, in everything, all at once
Whether you require smartness residing in the palm of your hand, under your TV, or in the industrial robots in your factory, we can help you achieve your vision. We enable this with our PowerVR neural network accelerators (NNAs) and graphics processing units (GPUs).
Our Neural Compute-SDK enables seamless deployment of artificial intelligence (AI) acceleration on our hardware IP, either in isolation or combined. Our NNA provides maximum efficiency with a scalable architecture to enable a wide range of smart edge and end-point devices, from high-efficiency IoT products to high-performance robotaxis.
Driving the AI revolution
Embedded intelligence at low power
For markets such as security, autonomous systems and retail, our AI cores are enabling intelligence as close to the edge as possible – at low power and low cost. Engineered to accelerate specific neural network applications, the PowerVR NNA delivers unmatched efficiency and performance.
IMG Series4 is a groundbreaking neural network accelerator (NNA) that offers high performance at ultra-low latency, architectural efficiency, and safety features. This makes it ideal for large-scale commercial implementation and is already silicon-proven in the automotive industry enabling next-generation ADAS and autonomous driving.
PowerVR Series3NX is a fast, power-efficient embedded solution for hardware acceleration of neural networks. Series3NX is a family of scalable cores performing up to 10 tera operations per second (TOPS). Thanks to key architectural enhancements Series3NX benefits from a 40% performance boost over the previous generation.
The power of AI processing
AI in action
See a demo of what Imagination is doing with AI technology.
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For more on AI, visit the Imagination blog where we apply our world-renowned engineering expertise to the latest news and insights.
Frequently asked questions
AI Accelerator chips come under many names such as Neural Network Accelerators (NNAs), Neural Processing Units (NPUs) and Machine Learning Engines. They are specialised processors designed to handle the complex computations required for artificial intelligence (AI) applications. Some examples of products that use AI accelerator chips include:
- Smartphones: Many high-end smartphones, such as the iPhone 12 and Samsung Galaxy S21, use AI accelerator chips to power features such as facial recognition, voice recognition, and augmented reality.
- Smart home devices: Smart home devices such as Amazon Echo and Google Nest Mini 2nd gen use AI accelerator chips to process voice commands and provide intelligent responses.
- Self-driving cars: Autonomous vehicles use AI accelerator chips to process sensor data and make real-time decisions based on the surrounding environment. Read more about AI in self-driving cars.
AI processors offer several benefits over traditional processors, including:
Faster performance: AI processors are designed to handle the complex computations required for AI workloads, such as deep learning and machine learning, much faster than traditional processors. This allows for more efficient processing of large datasets and faster training of AI models.
Energy efficiency: AI processors are optimised for processing large amounts of data in parallel, which can be done more energy-efficiently than traditional processors. This means that AI workloads can be processed more quickly and with less energy consumption helping companies achieve net zero.
Improved accuracy: AI processors are designed to handle the specific computations required for AI workloads, which can lead to improved accuracy in AI models. This is especially important in applications such as image recognition or natural language processing, where accuracy is critical.
Scalability: AI processors can be scaled more easily than traditional processors, which allows for faster processing of larger datasets and more complex AI models. This makes it possible to train and deploy AI models more quickly and efficiently.
Specialised design: AI processors are designed specifically for AI workloads, which means they can perform computations that would be difficult or impossible for traditional processors. This opens up new possibilities for AI applications, such as real-time object detection or speech recognition.
Overall, the benefits of AI processors make them essential for many AI applications, from self-driving cars to voice assistants to medical diagnosis tools.
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