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Found 7 papers, 4 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.
Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity
The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment.
YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems
We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU, ARM CPU, Nvidia GPU, NPU).
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.
Post-training deep neural network pruning via layer-wise calibration
We present a post-training weight pruning method for deep neural networks that achieves accuracy levels tolerable for the production setting and that is sufficiently fast to be run on commodity hardware such as desktop CPUs or edge devices.
Neural Network Compression Framework for fast model inference
In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF).
Ranked #3 on
Binarization
on ImageNet
Spikebench: An open benchmark for spike train time-series classification
Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks.
