82 citations · 239 across the 12 of their papers we have counts for
12 papers
Post-Training Sparsity-Aware Quantization
Gil Shomron, Freddy Gabbay, Samer Kurzum +1
Quantization is a technique used in deep neural networks (DNNs) to increase execution performance and hardware efficiency. Uniform post-training quantization (PTQ) methods are comm…
Post-Training BatchNorm Recalibration
Gil Shomron, Uri Weiser
We revisit non-blocking simultaneous multithreading (NB-SMT) introduced previously by Shomron and Weiser (2020). NB-SMT trades accuracy for performance by occasionally "squeezing"…
Semantic prefetching using forecast slices
Leeor Peled, Uri Weiser, Yoav Etsion
Modern prefetchers identify memory access patterns in order to predict future accesses. However, many applications exhibit irregular access patterns that do not manifest spatio-tem…
Non-Blocking Simultaneous Multithreading: Embracing the Resiliency of Deep Neural Networks
Gil Shomron, Uri Weiser
Deep neural networks (DNNs) are known for their inability to utilize underlying hardware resources due to hardware susceptibility to sparse activations and weights. Even in finer g…
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…
Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networks
Gil Shomron, Ron Banner, Moran Shkolnik +1
Convolutional neural networks (CNNs) introduce state-of-the-art results for various tasks with the price of high computational demands. Inspired by the observation that spatial cor…