AI Research
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.
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Neurips
Jan 29, 2026 | 1:30
Leading research and development across the entire spectrum of AI
Our key AI research papers
To develop impactful breakthroughs, our researchers aim to create the new state of the art in several key areas of AI research. Novel papers are one of the ways that we contribute impactful research to the larger AI community. Click below to view papers, as well as those papers with code.
Computer vision
Efficient architectures and algorithms for understanding images and videos.
Machine learning fundamentals
Foundational AI research like quantum, geometric, and Bayesian deep learning.
Neural Reasoning and Embodied AI
Causality, learning-to-search, and interaction with AI agents, robotics, reinforcement and imitation learning.
Wireless and RF Sensing
Understanding the world through deep learning applied to RF signals.
Power and Model Efficiency
Model design, compression, quantization, neural architecture search (NAS), compiler algorithms, and efficient hardware.
Speech, audio, and language processing
Keyword detection, speaker verification, speech recognition, sound detection, and contextual awareness.
Personalization and On-Device
Continuous learning, model adaptation, and privacy-preserving distributed learning.
Optimization and reinforcement learning
Learning to optimize through supervision, reinforcement learning, and Bayesian optimization.
Open collaboration with the AI community
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.
Qualcomm AI Research datasets
The better the data you have, the better you can train your AI models. Qualcomm Technologies has published a variety of datasets for research use — everything from hand gesture recognition to humans performing pre-defined, basic actions with everyday objects.
“We are advancing machine learning research across the entire spectrum of topics, including fundamental technology, platform innovation, and applied use cases. Our holistic systems-approach to full-stack AI is accelerating the pipeline from research to commercialization. We are excited by the potential of generative AI to transform our experience with technology.”
Jilei Hou
SVP Engineering, Qualcomm AI Research
We’re hiring.
Are you a machine learning specialist eager to start a new challenge? Are you a student looking to impact the global AI industry? Come join us as we make AI breakthroughs.
Life at Qualcomm AI Research
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Agentic AI distributed across Snapdragon devices
Sep 20, 2025 | 1:31
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AI Research - Purposeful innovation
Mar 31, 2022 | 3:22
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AI Research - Passionate execution
Mar 31, 2022 | 2:09
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AI Research - Openness
Mar 31, 2022 | 2:23
Demos
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World's fastest genAI video editing on a phone
Dec 7, 2024 | 0:40
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World’s first large multimodal model (LMM) on an Android phone
Feb 21, 2024 | 1:06
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Low rank adaptation (LoRA) on an Android phone
Feb 21, 2024 | 0:46
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World’s first large multimodal model (LMM) with audio reasoning on a Windows PC
Feb 21, 2024 | 1:15
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AI assistant with fast Llama 2 Chat 7B
Dec 4, 2023 | 4:43
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Word’s fastest Stable Diffusion running on a phone
Dec 4, 2023 | 3:01
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Enhanced video segmentation with on-device learning
Dec 4, 2023 | 3:28
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CVPR 2023 demo: ControlNet — Running a 1.5B parameter generative AI model completely on device
Jun 17, 2023 | 3:09
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Fitness coaching using an LLM grounded in real-time vision
Jun 17, 2023 | 3:12
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World’s fastest ControlNet demo running on a phone
Jun 17, 2023 | 0:27
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CVPR 2023 demo: 1080p neural video coding on a mobile device
Jun 17, 2023 | 3:39
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On-device Stable Diffusion
Feb 23, 2023 | 0:40
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NeurIPS 2022 demo: Conditional compute for on-device video understanding
Nov 22, 2022 | 2:11
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NeurIPS 2022 demo: Efficient real-time INT4 4K super-resolution on mobile
Nov 22, 2022 | 3:42
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NeurIPS 2022 demo: Teach your AI
Nov 22, 2022 | 2:25
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NeurIPS 2022 demo: Real-time on-device 3D reconstruction
Nov 22, 2022 | 2:54
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CVPR 2022 demo: Temporally-consistent video semantic segmentation
Nov 23, 2022 | 2:59
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Demo: Real-time and accurate self-supervised monocular depth estimation on mobile devices
Dec 3, 2021 | 6:12
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Demo: Federated learning framework for mobile
Dec 3, 2021 | 4:34
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Demo: Real-time neural video decoding on a mobile
Dec 3, 2021 | 4:58
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Demo: Weakly-supervised indoor Wi-Fi
Dec 3, 2021 | 3:28
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CVPR 2021 demo: Real-time neural video decoding on a mobile device
Jun 18, 2021 | 5:17
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NeurIPS 2020 Demo: On-device group-equivariant CNNs
Dec 4, 2020 | 4:11
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NeurIPS 2020 Demo: Efficient semantic segmentation of high-resolution video
Dec 4, 2020 | 5:11
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NeurIPS 2020 Demo: Neural network quantization with AdaRound
Dec 4, 2020 | 4:04
Webinars
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Gen AI Firsts - Pioneering research and leading proof-of-concepts
Aug 30, 2025 | 1:04:26
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Integrating Senses - Teaching AI to see hear, and interact
Oct 3, 2024 | 1:06:59
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Webinar - What's next in generative AI
Jul 19, 2024 | 1:14:22
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Webinar - Quantization unlocking scalability for LLMs
Jul 19, 2024 | 1:01:25
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Webinar - Efficient generative AI for images and videos
Apr 4, 2024 | 1:01:51
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Webinar - Generative AI at the edge
Nov 9, 2023 | 1:04:29
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Webinar – Advanced Compilation Technology for the AI Stack
Jun 23, 2023 | 59:54
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Webinar - Solving unsolvable combinatorial problems with AI
Feb 6, 2023 | 1:00:55
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Webinar – Model Efficiency for Edge AI
Sep 28, 2022 | 57:48
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Webinar — 3D perception: Cutting-edge AI research to understand the 3D world
Jul 21, 2022 | 1:05:05
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Webinar: Bringing AI research to wireless communications and sensing
Jun 1, 2022 | 1:07:32
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Webinar — AI firsts: Leading from research to proof-of-concepts
Mar 16, 2022 | 1:01:06
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Webinar — How AI research is enabling next-gen codecs
Jul 19, 2021 | 1:01:33
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Webinar — Enabling on-device learning at scale
Oct 29, 2021 | 1:02:34
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Webinar — Intelligence at Scale through AI Model Efficiency
Apr 8, 2021 | 1:01:13
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Webinar — Efficient video perception through AI
Jan 23, 2021 | 1:02:50
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Webinar — Pushing the boundaries of AI research
Sep 9, 2020 | 1:00:23
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Webinar — Enabling power-efficient AI through quantization
May 2, 2020 | 1:01:18
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Webinar — 5G+AI: The Ingredients Fueling Tomorrow's Technology Innovations
Feb 20, 2020 | 58:52
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The future of AI is hybrid.
As generative AI adoption grows at record-setting speeds and drives higher demand for compute, AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential. Beyond cost, a hybrid AI architecture offers the additional benefits of performance, personalization, privacy, and security. The cloud and edge devices work together to deliver more powerful, efficient, and highly optimized AI. The hybrid AI approach is applicable to virtually all generative AI applications and device segments – including phones, laptops, XR headsets, cars, and IoT.
We are making hybrid AI a reality.
Qualcomm is enabling intelligent computing everywhere. As the on-device AI leader, Qualcomm Technologies is uniquely positioned to scale hybrid AI with industry-leading hardware and software solutions across billions of edge devices. Our hardware offers industry-leading performance per watt, and our perpetual flywheel of innovation keeps us at the forefront of on-device AI solutions. With our technology leadership, global scale, and ecosystem enablement, Qualcomm Technologies is making hybrid AI a reality.
Presentation - What's next in on-device AIMay 30, 2024
Presentation - Quantization: unlocking scalability for large language modelsJun 13, 2024
Presentation — Efficient generative AI for images and videosJan 25, 2024
Presentation — Generative AI at the edgeNov 9, 2023
Presentation — Making AI ubiquitousOct 1, 2023
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