WEBINAR: Creating A Hardware-Optimised Neural Rendering Solution

AI is reshaping graphics, but achieving great results requires more than AI acceleration alone. In this session, we’ll explore how hardware and software can be co-designed to create more efficient neural rendering solutions.

Using Imagination’s new Neural Super Resolution as a practical example, we’ll show how we developed a technique that delivers high-quality visual experiences while minimising bandwidth, latency and power consumption. We’ll also present how we leveraged the innovative self-compression technique to reduce model size by up to 65%, enabling sophisticated neural graphics on resource-constrained edge devices.

By co-designing hardware, software and neural networks, it’s possible to unlock breakthrough visual quality while making every byte, cycle and milliwatt count.

Presented by James Imber. Hosted by SemiWiki.

Dr. James Imber is Director of Research at Imagination Technologies, with 14 years of experience in the semiconductor IP industry. His team’s work spans network compression, embedded AI inference and algorithm design for resource-constrained systems. James has expertise in neural graphics, quantization-aware training, edge perception, numerical optimization and classical computer vision. Prior to his current role, he worked extensively with NPUs, ISPs and GPUs, driving innovation in embedded AI and advanced imaging technologies. James holds a PhD from the University of Surrey’s Centre for Vision, Speech and Signal Processing.

DISCOVER E-SERIES

E-Series combines rendering excellence with programmable AI acceleration that sits efficiently alongside the GPU’s rendering pipelines and comes with support for low-precision number formats.

The result is one processor and one software stack that can run workloads as diverse as generative AI, neural rendering and gaming, either independently or alongside CPUs and other accelerators.

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