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.

Power efficiency

Broadcasted Residual Learning for Efficient Keyword Spotting | Interspeech 2021

PQK: Model Compression for Computational Resource Constraint via Pruning, Quantization and Knowledge Distillation | Interspeech 2021

Prototype-based Personalized Pruning | ICASSP 2021

Skip-Convolutions for Efficient Video Processing | CVPR 2021

FrameExit: Conditional Early Exiting for Efficient Video Recognition | CVPR 2021

Structured Convolutions for Efficient Neural Network Design | NeurIPS 2020

Bayesian Bits: Unifying Quantization and Pruning | NeurIPS 2020

A Data and Compute Efficient Design for Limited-Resources Deep Learning | ICLR 2020

LSQ+: Improving low-bit quantization through learnable offsets and better initialization | CVPR 2020

Conditional Channel Gated Networks for Task-Aware Continual Learning | CVPR 2020

Gradient l1 Regularization for Quantization Robustness | ICLR 2020

Batch-Shaping for Learning Conditional Channel Gated Networks  | ICLR 2020

Data-Free Quantization through Weight Equalization and Bias Correction | ICCV 2019

Up or Down? Adaptive Rounding for Post-Training Quantization | ICML 2020

Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets | ICLR 2019

DAC: Data-free Automatic Acceleration of Convolutional Networks | WACV 2019

Relaxed Quantization for Discretized Neural Networks | ICLR 2019

Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks | RMS

A Quantization-Friendly Separable Convolution for MobileNets  | EMC^2 2018

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