Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

Transformer based architectures have become de-facto models used for a range of Natural
Language Processing tasks. In particular, the BERT based models achieved significant …

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 …

We introduce a light-weight, power efficient, and general purpose convolutional neural
network, ESPNetv2, for modeling visual and sequential data. Our network uses group point …

The recent advancements in self-supervised learning, combined with the Transformer
architecture, have enabled natural language processing (NLP) to achieve remarkably low …

Transformer-based architectures have become the de-facto standard models for a wide
range of Natural Language Processing tasks. However, their memory footprint and high …

Deep neural networks are the state-of-the-art methods for many real-world tasks, such as
computer vision, natural language processing and speech recognition. For all its popularity …

In recent years, the fields of natural language processing (NLP) and information retrieval (IR)
have made tremendous progress thanks to deep learning models like Recurrent Neural …