Qualcomm AI Research

At Qualcomm AI Research, we are advancing AI to make its core capabilities – perception, reasoning, and action – ubiquitous across devices. Our mission is to make breakthroughs in fundamental AI research and scale them across industries. By bringing together some of the best minds in the field, we’re pushing the boundaries of what’s possible and shaping the future of AI.

AI Research Video

Jul 23, 2021


Leading research and development across the entire spectrum of AI

Our key AI research areas

Computer vision

Efficient architectures and algorithms for understanding images and videos.

Radar and RF sensing

Understanding the world through deep learning applied to RF signals.

Power efficiency

Model design, compression, quantization, neural architecture search (NAS), compiler algorithms, and efficient hardware.

Machine learning fundamentals

Foundational AI research like quantum, geometric, and Bayesian deep learning.

Speech, audio, and language processing

Keyword detection, speaker verification, speech recognition, sound detection, and contextual awareness.

Data compression and generative modeling

Learning low-dimensional representations for data such as video and speech.

Personalization and federated learning

Continuous learning, model adaptation, and privacy-preserving distributed learning.

Optimization and reinforcement learning

Learning to optimize through supervision, reinforcement learning, and Bayesian optimization.

AI Research Papers

Novel papers are one of the ways Qualcomm Technologies contributes impactful research to the larger community of AI research. Below are papers that Qualcomm AI Research has written or co-authored.

Open source project: AI Model Efficiency Toolkit (AIMET)

Qualcomm Innovation Center open sourced AIMET, which includes state-of-the-art quantization and compression techniques. The goal for this open source project is to collaborate with other leading AI researchers, provide a simple library plugin for AI developers, and help migrate the ecosystem toward integer inference.

Demo: Real-time and accurate self-supervised monocular depth estimation on mobile devices

Dec 2, 2021


Demo: Federated learning framework for mobile

Dec 3, 2021


Demo: Real-time neural video decoding on a mobile

Dec 3, 2021


Demo: Weakly-supervised indoor Wi-Fi

Dec 3, 2021


Webinar - How AI research is enabling next-gen codecs

Jul 19, 2021


Webinar : Enabling on-device learning at scale

Oct 29, 2021


CVPR 2021 demo: Real-time neural video decoding on a mobile device

Jun 18, 2021


Webinar : Intelligence at Scale through AI Model Efficiency

Apr 8, 2021


Webinar: Efficient video perception through AI

Jan 22, 2021


NeurIPS 2020 Demo: On-device group-equivariant CNNs

Dec 3, 2020


NeurIPS 2020 Demo: Efficient semantic segmentation of high-resolution video

Dec 3, 2020


NeurIPS 2020 Demo: Neural network quantization with AdaRound

Dec 12, 2020


Webinar: Pushing the boundaries of AI research

Sep 9, 2020


Webinar: Enabling power-efficient AI through quantization

May 1, 2020


Webinar: 5G+AI: The Ingredients Fueling Tomorrow's Technology Innovations

Feb 19, 2020



We’re hiring.

Are you a machine learning specialist eager to start a new challenge and have a global impact on the industry? Come join us as we make AI breakthroughs.

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Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc.
AI Model Efficiency Toolkit is a product of Qualcomm Innovation Center, Inc.
Qualcomm Innovation Center, Inc. is a wholly-owned subsidiary of Qualcomm Technologies, Inc.


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