In this paper, we find that the conventional convolution operation becomes the bottleneck for
extremely efficient binary neural networks (BNNs). To address this issue, we open up a new …

Spiking neural networks are efficient computation models for low-power environments.
Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful …

We introduce NeuralVDB, which improves on an existing industry standard for efficient
storage of sparse volumetric data, denoted VDB [Museth], by leveraging recent …

Recommender systems have become ubiquitous in the past years. They solve the tyranny of
choice problem faced by many users, and are utilized by many online businesses to drive …

Recent years have witnessed rapid advances in learnt video coding. Most algorithms have
solely relied on the vector-based motion representation and resampling (eg, optical flow …

The increasing demand for video streaming services is the key driver of modern wireless
and mobile communications. Although many studies have designed digital-based delivery …

This paper focuses on the energy optimization problem of traction substations. The paper
addresses the difficulty of considering time-varying parameters and environmental …

Deploying a lightweight deep model for scene text recognition task on mobile devices has
great commercial value. However, the conventional softmax-based one-hot classification …

We present a novel, deep Q-learning based method, QLP, for pruning deep neural networks
(DNNs). Given a DNN, our method intelligently determines favorable layer-wise sparsity …

Pruning neural networks has become popular in the last decade when it was shown that a
large number of weights can be safely removed from modern neural networks without …