research
∙
05/23/2021
Post-Training Sparsity-Aware Quantization
Quantization is a technique used in deep neural networks (DNNs) to incre...
share
research
∙
10/12/2020
Post-Training BatchNorm Recalibration
We revisit non-blocking simultaneous multithreading (NB-SMT) introduced ...
research
∙
05/13/2020
Semantic prefetching using forecast slices
Modern prefetchers identify memory access patterns in order to predict f...
research
∙
04/17/2020
Non-Blocking Simultaneous Multithreading: Embracing the Resiliency of Deep Neural Networks
Deep neural networks (DNNs) are known for their inability to utilize und...
research
∙
02/18/2020
Robust Quantization: One Model to Rule Them All
Neural network quantization methods often involve simulating the quantiz...
research
∙
09/17/2019
Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networks
Convolutional neural networks (CNNs) introduce state-of-the-art results ...
research
∙
07/21/2018
Exploiting Spatial Correlation in Convolutional Neural Networks for Activation Value Prediction
Convolutional neural networks (CNNs) compute their output using weighted...
research
∙
03/19/2018
Towards Memory Prefetching with Neural Networks: Challenges and Insights
Accurate memory prefetching is paramount for processor performance, and ...
research
∙
05/19/2017