11 citations · 21 across the 3 of their papers we have counts for
12 papers
Energy awareness in low precision neural networks
Nurit Spingarn Eliezer, Ron Banner, Elad Hoffer +2
Power consumption is a major obstacle in the deployment of deep neural networks (DNNs) on end devices. Existing approaches for reducing power consumption rely on quite general prin…
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks
Itay Hubara, Brian Chmiel, Moshe Island +3
Unstructured pruning reduces the memory footprint in deep neural networks (DNNs). Recently, researchers proposed different types of structural pruning intending to reduce also the…
GAN "Steerability" without optimization
Nurit Spingarn-Eliezer, Ron Banner, Tomer Michaeli
Recent research has shown remarkable success in revealing "steering" directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful ima…
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
Itay Hubara, Yury Nahshan, Yair Hanani +2
Lately, post-training quantization methods have gained considerable attention, as they are simple to use, and require only a small unlabeled calibration set. This small dataset can…
Neural gradients are near-lognormal: improved quantized and sparse training
Brian Chmiel, Liad Ben-Uri, Moran Shkolnik +3
While training can mostly be accelerated by reducing the time needed to propagate neural gradients back throughout the model, most previous works focus on the quantization/pruning…
Robust Quantization: One Model to Rule Them All
Moran Shkolnik, Brian Chmiel, Ron Banner +4
Neural network quantization methods often involve simulating the quantization process during training, making the trained model highly dependent on the target bit-width and precise…