PowerVR

Series3NX

Advanced Compute and Neural Network processors enabling the Smart Edge

The high performance neural network inference accelerator

Neural networks are essential for complex signal processing and pattern recognition that lie behind many AI technologies. PowerVR Series3NX is a power-efficient embedded solution for hardware acceleration of neural networks. Thanks to key architectural enhancements the Series3NX benefits from a 40% performance boost over the previous generation, performing up to 10 tera operations per second (TOPS) from a single core.

AI Processors

Bringing multi-core scalability to the embedded AI market

Thanks to the scalability of PowerVR Series3NX architecture, multi-core implementations can achieve up to 160 TOPS, enabling high performance for the demanding applications. Series3NX will be available in a variety of offerings, enabling SoC manufacturers to meet a range of design targets to address multiple markets and applications.

IMG B-Series GPU and PowerVR Series3NX NNA cores have been licensed for the latest RISC-V-based SoCs of Alibaba Group’s T-Head Semiconductor for AIoT applications.

Learn More

Neural network acceleration for edge devices

As neural networks drive an explosion in technological progress across industries, NNAs are a fundamental class of processor as significant as CPUs and GPUs. By integrating a Series3NX Neural Network Accelerator (NNA), manufacturers can build devices that offer fast computation of neural networks at very low power consumption, in minimal silicon area. Offering this processing in edge devices removes the limitations of the cloud, such as bandwidth constraints, latency issues and privacy concerns.

Flexible bit-depth support

Serving as a flexible solution, the Series3NX supports neural network bit depths from 16-bit down to 4-bit, reducing bandwidth and increasing performance without compromising inference accuracy.

Lossless weight compression

Complementing its low-bit depth support, the Series3NX features a lossless weight compression scheme that reduces network model sizes and bandwidth thus increasing overall performance

Security enablement

The Series3NX integrates with the industry-leading security architectures with a flexible infrastructure that enables integration into custom solutions – allowing right holders to protect their content where required.

Low power consumption

With an outstanding inference/mW, the Series3NX delivers neural network acceleration with low power consumption.

Putting the smart in smartphone

In mobile devices where a GPU is mandated, device manufacturers can pair a PowerVR Series9XE/XEP or 9XM/9XMP GPU with the Series3NX NNA in the same silicon footprint as a competing standalone GPU. Machine learning is now deployed in a wide variety of mobile applications, such as face recognition and verification, object recognition, image enhancement, style transfer and music tagging to name but a few. To support this, our Series3NX NNA cores deliver a paradigm shift in performance, while simultaneously reducing battery consumption over pure GPU solutions.

Security & surveillance

PowerVR Series3NX NNA cores enable a new class of smart camera that perform high-performance neural network-based analytics for a wide range of verticals such as commercial and home surveillance, retail analytics and drones. It supports classic use cases such as number/license plate recognition, person/object recognition, behaviour detection and perimeter defence.

Automotive (ADAS)

Convolutional neural networks (CNNs) are playing a crucial role in developing self-driving cars. The Series3NX NNAs will power advanced driver-assistance systems (ADAS) including driver alertness monitoring, driver gaze tracking, seat occupancy, road-sign detection, drivable path analysis, road user detection and driver recognition.

Augmented & Virtual reality

Neural network hardware acceleration will be critical to fulfil the potential of next-gen augmented and virtual reality use cases. Scene understanding will enhance augmented reality, while movement analysis, eye tracking and gesture recognition will provide context awareness in virtual reality to provide the best possible relative user experiences.