OnQ Blog
Feb 4, 2020Qualcomm 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
Our key AI research areas
Power efficiency
Model design, compression, quantization, activation, algorithms, and efficient hardware.
Personalization
Continuous learning, model adaptation, and privacy-preserved distributed learning.
Efficient learning
Robust learning through minimal data, unsupervised learning, and on-device learning.
System architecture
Multi-task and multi-modal learning, sensor fusion, and cloud-edge systems.
AI Research Video
Dec 6, 2019
2:46
Leading research and development across the entire spectrum of AI
AI Research Papers
Novel papers are one of the ways Qualcomm contributes impactful research to the larger community of AI research. Below are papers that Qualcomm AI Research has written or co-authored.
Efficient Learning
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Combinatorial Bayesian Optimization using Graph Cartesian Product | NeurIPS 2019
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A General Theory of Equivariant CNNs on Homogeneous Spaces | NeurIPS 2019
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Recognizing Compressed Videos: Challenges and Promises | ICCV 2019
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Weakly-supervised Degree of Eye-closeness Estimation | ICCV 2019
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Video Compression With Rate-Distortion Autoencoders | ICCV 2019
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Video2vec Embeddings Recognize Events when Examples are Scarce | IEEE 2017
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Gauge Equivariant Convolutional Networks and the Icosahedral CNN | ICML 2019
Power Efficiency
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Data-Free Quantization through Weight Equalization and Bias Correction | ICCV 2019
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A Quantization-Friendly Separable Convolution for MobileNets | EMC^2 2018
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Relaxed Quantization for Discretized Neural Networks | ICLR 2019
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Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets | ICLR 2019
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DAC: Data-free Automatic Acceleration of Convolutional Networks | WACV 2019
The latest
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.