About InterDigital
InterDigital is a global research and development company focused primarily on wireless, video, artificial intelligence (“AI”), and related technologies. We design and develop foundational technologies that enable connected, immersive experiences in a broad range of communications and entertainment products and services. We license our innovations worldwide to companies providing such products and services, including makers of wireless communications devices, consumer electronics, IoT devices, cars and other motor vehicles, and providers of cloud-based services such as video streaming. As a leader in wireless technology, our engineers have designed and developed a wide range of innovations that are used in wireless products and networks, from the earliest digital cellular systems to 5G and today’s most advanced Wi-Fi technologies. We are also a leader in video processing and video encoding/decoding technology, with a significant AI research effort that intersects with both wireless and video technologies. Founded in 1972, InterDigital is listed on Nasdaq.
InterDigital is a registered trademark of InterDigital, Inc.
For more information, visit: www.interdigital.com .
Summary
DeepPVR is InterDigital’s in-house deep-learning algorithm that reduces the energy consumed by images shown on OLED displays and on LCD displays with local dimming, while preserving perceived picture quality. The current implementation delivers the expected power savings but does not run in real time, mainly because of implementation inefficiencies rather than fundamental algorithmic limits. The intern will join the Video research team to make DeepPVR run in real time on modern GPUs and on mobile devices equipped with neural accelerators (TPU/NPU). The work spans inference-pipeline optimization, network-architecture modifications such as pruning and quantization, and an experimental study of the battery-life impact of running the algorithm live on a mobile device.
Responsibilities
Profile the existing DeepPVR implementation to identify compute, memory and data-transfer bottlenecks.
Re-implement and optimize the inference pipeline for real-time execution on modern GPUs (e.g., CUDA, TensorRT, ONNX Runtime).
Port the model to mobile devices with TPU/NPU accelerators using on-device inference frameworks (e.g., LiteRT/TensorFlow Lite, Core ML, Qualcomm AI Engine) and optimize it for real-time throughput.
Explore network-architecture modifications such as pruning, quantization, knowledge distillation and lightweight backbones, and quantify their impact on runtime, power savings and visual quality.
Design and run experiments measuring the battery-life impact of running DeepPVR in real time on mobile devices, separating display power savings from the added compute cost.
Document the work, present results to the team, and contribute to publications or invention disclosures where relevant.
Qualifications
Currently enrolled in a Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics or a related field.
Strong programming skills in Python and C/C++.
Experience with a deep-learning framework (PyTorch or TensorFlow) and with training and evaluating convolutional networks.
Experience with GPU programming or inference optimization (CUDA, TensorRT, ONNX, LiteRT/TensorFlow Lite, Core ML) is a strong plus.
Familiarity with model-compression techniques (pruning, quantization, distillation) is a plus.
Knowledge of image processing, display technologies (OLED, LCD local dimming) or perceptual image quality is a plus.
Experience with mobile development (Android/iOS), on-device benchmarking or power measurement is a plus.
Good communication skills in English. Autonomous, curious and rigorous in experimental work. French communication skills is a plus.
Keywords:
Deep learning, model compression, pruning, quantization, real-time inference, GPU, TPU/NPU, edge AI, display power saving, energy efficiency, image processing.
Expected…