Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

Due to complex interactions among various deep neural network (DNN) optimization
techniques, modern DNNs can have weights and activations that are dense or sparse with …

Sparse deep neural network (DNN) has become an important technique for reducing the
inference cost of large DNNs. However, computing large sparse DNNs is very challenging …

The invention of Transformer model structure boosts the performance of Neural Machine
Translation (NMT) tasks to an unprecedented level. Many previous works have been done to …

Approximate computing (AxC) has advanced from being an emerging design paradigm to
becoming one of the most popular and effective methods of energy optimization for …

In this paper we present a method called Elite BackProp (EBP) to train more interpretable
convolutional neural networks (CNNs) by introducing class-wise activation sparsity; after …

Brain-inspired event-based processors have attracted considerable attention for edge
deployment because of their ability to efficiently process Convolutional Neural Networks …

Over the past several years, the need for on-device deep learning has been rapidly
increasing, and efficient CNN inference on mobile platforms has been actively researched …

There has been significant focus on creating neuro-symbolic models for interpretable image
classification using Convolutional Neural Networks (CNNs). These methods aim to replace …