The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …

The advent of machine learning (ML) methods for the industry has opened new possibilities
in the automotive domain, especially for Advanced Driver Assistance Systems (ADAS) …

To accelerate the practical applications of artificial intelligence, this paper proposes a high
efficient layer‐wise refined pruning method for deep neural networks at the software level …

Convolutional Neural Networks (CNNs) have a major impact on our society, because of the
numerous services they provide. These services include, but are not limited to image …

High-throughput and low-latency Convolutional Neural Network (CNN) inference is
increasingly important for many cloud-and edge-computing applications. FPGA-based …

The acceleration of deep neural networks (DNNs) on edge devices is gaining significant
importance in various application domains. General purpose graphics processing units …

In recent years, dedicated hardware accelerators for the acceleration of the convolutional
neural network (CNN) have been extensively studied. Although many studies have …

Purpose This paper aims to review in-memory computing (IMC) for machine learning (ML)
applications from history, architectures and options aspects. In this review, the authors …

“Cloud-native” is the umbrella adjective describing the standard approach for developing
applications that exploit cloud infrastructures' scalability and elasticity at their best. As the …

Heterogeneous computing platforms are now a valuable solution to continue to meet
Service Level Agreements (SLAs) for compute intensive cloud workloads. Field …