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Spring 2023
Predicting a dense depth map from LiDAR scans and synced RGB images with a small deep neural network is a challenging task. Most top-accuracy methods boost precision by having a very large number of parameters and as a result huge memory consumption. Whereas, depth completion tasks are commonly...
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Mixed Low-bit Quantization for Model Compression with Layer Importance and Gradient Estimations
DownloadSpring 2022
Deep neural networks (DNNs) have been widely used in the modern world in recent years. However, due to the substantial memory consumption and high computational power use of DNNs, deploying them on devices with limited resources is challenging. Model compression methods can provide us with a...
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Fall 2022
Deep neural networks (DNN) have emerged as the state-of-the-art method in several research areas. DNN is yet to fully permeate resource-constrained computing platforms, such as mobile phones. Accurate DNN models being deeper and wider take considerable memory and time to execute on small devices...