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Found 5 papers, 1 papers with code
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy.
One Weight Bitwidth to Rule Them All
Weight quantization for deep ConvNets has shown promising results for applications such as image classification and semantic segmentation and is especially important for applications where memory storage is limited.
On the Pareto Efficiency of Quantized CNN
Weight Quantization for deep convolutional neural networks (CNNs) has shown promising results in compressing and accelerating CNN-powered applications such as semantic segmentation, gesture recognition, and scene understanding.
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)
Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost.
PACT: Parameterized Clipping Activation for Quantized Neural Networks
We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets.
