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 advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

On-device training enables the model to adapt to new data collected from the sensors by
fine-tuning a pre-trained model. Users can benefit from customized AI models without having …

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 …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

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 …

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 …

Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …