4 papers · 1 filter
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"…
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…