Cloud computing platforms can provide the compu-tational resources required for training
large machine learning models such as deep neural networks. While the pay-as-you-go …

Artificial intelligence has achieved significant success in handling complex tasks in recent
years. This success is due to advances in machine learning algorithms and hardware …

As intelligence recently moves to the edge to tackle the problems of privacy, scalability, and
network bandwidth in the centralized intelligence, it is necessary to construct an efficient yet …

Lightweight convolutional neural networks (CNNs) enable lower inference latency and data
traffic, facilitating deployment on resource-constrained edge devices such as field …

In recent years, the field of Deep Learning has seen many disruptive and impactful
advancements. Given the increasing complexity of deep neural networks, the need for …

Distributed machine learning is a mainstream system to learn insights for analytics and
intelligence services of many fronts (eg, health, streaming and business) from their massive …

State-of-the-art optimization is steadily shifting towards massively parallel pipelines with
extremely large batch sizes. As a consequence, CPU-bound preprocessing and …

Data center networks (DCNs) have widely deployed RDMA to support data-intensive
applications such as machine learning. While DCNs are designed with rich multi-path …

In real-world applications, audio surveillance is often performed by large models that can
detect many types of anomalies. However, typical approaches are based on centralized …

The time required for training the neural networks increases with size, complexity, and
depth. Training model parameters by backpropagation inherently creates feedback loops …