Distributed training is a novel approach to accelerating training of Deep Neural Networks
(DNN), but common training libraries fall short of addressing the distributed nature of …

This article proposes EncoDeep, an end-to-end framework that facilitates encoding, bitwidth
customization, fine-tuning, and implementation of neural networks on FPGA platforms …

Over the years, interest in computational storage devices has been growing steadily. This is
largely due to the rise of data-intensive applications, such as machine learning, online video …

As solid-state drives (SSDs) with sufficient computing power have recently become the
dominant devices in modern computer systems, in-storage processing (ISP), which …

Near-data processing (NDP) architecture is promised to break the bottleneck of data
movement in many scenarios (eg, databases and recommendation systems), which limits …

Convolutional Neural Networks (CNNs) are now quintessential kernels within embedded
computer vision applications deployed in edge devices. Heterogeneous Multi-Processor …

Heterogeneous computing is growing as an important hardware and software paradigm,
both in high-performance computing and in application computing in general. Nevertheless …

Although recent scaling up approaches to train deep neural networks have proven to be
effective, the computational intensity of large and complex models, as well as the availability …

Computational storage drives (CSD) are solid-state drives (SSD) empowered by general-
purpose processors that can perform in-storage processing. They have the potential to …

While (1) serverless computing is emerging as a popular form of cloud execution,
datacenters are going through major changes:(2) storage dissaggregation in the system …