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.

Qualcomm AI Research Introduction

Jun 8, 2018

2:27

“Machine learning algorithms such as deep learning already use large amounts of energy. And with AI increasingly moving to power-constrained edge devices, energy efficiency will only become more important. The benchmark that matters will be how much intelligence one can squeeze out of every joule of energy. I believe that Qualcomm is uniquely positioned to address this problem and be a key player in AI.”  

- Professor Dr. Max Welling
VP of Technology, Qualcomm Technologies Netherlands B.V.

Leading research and development across the entire spectrum of AI

Download the presentation

Watch the webinar

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.

Computer vision

Data compression and generative modeling

Machine learning fundamentals

Optimization and reinforcement learning

Personalization and federated learning

Power efficiency

Radar and RF sensing

Speech, audio, and language processing

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.

Webinar: Efficient video perception through AI

Jan 22, 2021

1:02:50

NeurIPS 2020 Demo: On-device group-equivariant CNNs

Dec 3, 2020

4:11

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

Dec 3, 2020

5:11

NeurIPS 2020 Demo: Neural network quantization with AdaRound

Dec 12, 2020

4:04

Webinar: Pushing the boundaries of AI research

Sep 9, 2020

1:00:23

Webinar: Enabling power-efficient AI through quantization

May 1, 2020

1:01:18

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

Feb 19, 2020

58:52

AI Research Video

Dec 6, 2019

2:46

The latest

Press Note

Dec 18, 2020

Qualcomm Showcases Leading Performance on New Flagship Snapdragon 888 5G Mobile Platform
Qualcomm Showcases Leading Performance on New Flagship Snapdragon 888 5G Mobile Platform
Qualcomm Showcases Leading Performance on New Flagship Snapdragon 888 5G Mobile Platform
Qualcomm Showcases Leading Performance on New Flagship Snapdragon 888 5G Mobile Platform
Qualcomm Showcases Leading Performance on New Flagship Snapdragon 888 5G Mobile Platform
Qualcomm Showcases Leading Performance on New Flagship Snapdragon 888 5G Mobile Platform

OnQ Blog

Dec 2, 2020

Exploring the AI capabilities of the Qualcomm Snapdragon 888 Mobile Platform [video]
Exploring the AI capabilities of the Qualcomm Snapdragon 888 Mobile Platfor....
Exploring the AI capabilities of the Qualcomm Snapdragon 888 Mobile Platfor....
Exploring the AI capabilities of the Qualcomm Snapdragon 888 Mobile Platform [video]
Exploring the AI capabilities of the Qualcomm Snapdragon 888 Mobile Platform [video]
Exploring the AI capabilities of the Qualcomm Snapdragon 888 Mobile Platform [video]

Documents

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.

Explore our open AI positions

Stay informed with our mobile computing technology newsletter. 

Sign up

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.

 

About QualcommCareersOfficesContact UsSupportEmail Subscriptions
Terms of UsePrivacyCookies Policy
Cookie Settings

©2021 Qualcomm Technologies, Inc. and/or its affiliated companies.

References to "Qualcomm" may mean Qualcomm Incorporated, or subsidiaries or business units within the Qualcomm corporate structure, as applicable.

Qualcomm Incorporated includes Qualcomm's licensing business, QTL, and the vast majority of its patent portfolio. Qualcomm Technologies, Inc., a wholly-owned subsidiary of Qualcomm Incorporated, operates, along with its subsidiaries, substantially all of Qualcomm's engineering, research and development functions, and substantially all of its products and services businesses. Qualcomm products referenced on this page are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

Materials that are as of a specific date, including but not limited to press releases, presentations, blog posts and webcasts, may have been superseded by subsequent events or disclosures.

Nothing in these materials is an offer to sell any of the components or devices referenced herein.