Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

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 …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

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 …

The advent of Deep Learning (DL) has radically transformed the computing industry across
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …

A recent trend in deep neural network (DNN) development is to extend the reach of deep
learning applications to platforms that are more resource and energy-constrained, eg …

Abstract Model compression is an effective technique to efficiently deploy neural network
models on mobile devices which have limited computation resources and tight power …

At Facebook, machine learning provides a wide range of capabilities that drive many
aspects of user experience including ranking posts, content understanding, object detection …

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous
in recent years. This has led to the generation of large quantities of data in real-time, which …