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Found 7 papers, 1 papers with code
QGen: On the Ability to Generalize in Quantization Aware Training
In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance.
DeepliteRT: Computer Vision at the Edge
The proliferation of edge devices has unlocked unprecedented opportunities for deep learning model deployment in computer vision applications.
DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables
A lot of recent progress has been made in ultra low-bit quantization, promising significant improvements in latency, memory footprint and energy consumption on edge devices.
Accelerating Deep Learning Model Inference on Arm CPUs with Ultra-Low Bit Quantization and Runtime
Deep Learning has been one of the most disruptive technological advancements in recent times.
QReg: On Regularization Effects of Quantization
To confirm our analytical study, we performed an extensive list of experiments summarized in this paper in which we show that the regularization effects of quantization can be seen in various vision tasks and models, over various datasets.
Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers.
U-Net Fixed-Point Quantization for Medical Image Segmentation
We then apply our quantization algorithm to three datasets: (1) the Spinal Cord Gray Matter Segmentation (GM), (2) the ISBI challenge for segmentation of neuronal structures in Electron Microscopic (EM), and (3) the public National Institute of Health (NIH) dataset for pancreas segmentation in abdominal CT scans.
