Memristor based neuromorphic computing systems give alternative solutions to boost the
computing energy efficiency of Neural Network (NN) algorithms. Because of the large-scale …

Heterogeneous systems-on-chip (SoCs) are highly favorable computing platforms due to
their superior performance and energy efficiency potential compared to homogeneous …

In the age of artificial intelligence (AI), the huge data movements between memory and
computing units become the bottleneck of von Neumann architectures, ie, the “memory wall” …

Domain-specific SoCs (DSSoCs) are an attractive solution for domains with extremely
stringent power, performance, and area constraints. However, DSSoCs suffer from two …

With the widespread use of Deep Neural Networks (DNNs), machine learning algorithms
have evolved in two diverse directions—one with ever-increasing connection density for …

Movement disorders, such as Parkinson's disease, affect more than 10 million people
worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these …

Priority-aware networks-on-chip (NoCs) are used in industry to achieve predictable latency
under different workload conditions. These NoCs incorporate deflection routing to minimize …

The massive growth of the Internet of Things (IoT) applications and the challenges of Cloud
computing have increased the importance of Fog networks for timely processing the …

Networks-on-Chip (NoCs) used in commercial many-core processors typically incorporate
priority arbitration. Moreover, they experience bursty traffic due to application workloads …

Designing hardware accelerators for machine learning (ML) applications is a well-
researched problem. This article presents a tutorial regarding new computing architectures …