50 citations · 50 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
The Knowledge Within: Methods for Data-Free Model Compression
Matan Haroush, Itay Hubara, Elad Hoffer +1
Recently, an extensive amount of research has been focused on compressing and accelerating Deep Neural Networks (DNN). So far, high compression rate algorithms require part of the…
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?
Niv Giladi, Mor Shpigel Nacson, Elad Hoffer +1
Background: Recent developments have made it possible to accelerate neural networks training significantly using large batch sizes and data parallelism. Training in an asynchronous…
Augment your batch: better training with larger batches
Elad Hoffer, Tal Ben-Nun, Itay Hubara +3
Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. W…
Scalable Methods for 8-bit Training of Neural Networks
Ron Banner, Itay Hubara, Elad Hoffer +1
Quantized Neural Networks (QNNs) are often used to improve network efficiency during the inference phase, i.e. after the network has been trained. Extensive research in the field s…