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Found 9 papers, 2 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.
Statistical Hardware Design With Multi-model Active Learning
For instance, in our performance prediction setting, the proposed method needs 65% fewer samples to create the model, and in the design space exploration setting, our proposed method can find the best parameter settings by exploring less than 50 samples.
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.
Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose Estimation
Spacecraft pose estimation is an essential computer vision application that can improve the autonomy of in-orbit operations.
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators
Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural network (DNN) inference accelerators.
Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks
We propose to use a multi-dimensional Pareto frontier to re-define the efficiency measure of candidate deep learning models, where several variables such as training cost, inference latency, and accuracy play a relative role in defining a dominant model.
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA
This is due to the lower power consumption and easy reconfigurability offered by these platforms.
Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks
Deep neural networks (DNNs) depend on the storage of a large number of parameters, which consumes an important portion of the energy used during inference.
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.
