Employing convolutional neural networks (CNNs) in embedded devices seeks novel low-
cost and energy efficient CNN accelerators. Stochastic computing (SC) is a promising low …

Neuromorphic computing systems are an emerging field that takes its inspiration from the
biological neural architectures and computations inside the mammalian nervous system …

Spiking neural networks (SNNs) are artificial neural network models that more closely mimic
biological neural networks. In addition to neuronal and synaptic state, SNNs incorporate the …

Parallel hardware accelerators for large-scale neural networks typically consist of several
processing nodes, arranged as a multi-or many-core system-on-chip, connected by a …

The astonishing development in the field of artificial neural networks (ANN) has brought
significant advancement in many application domains, such as pattern recognition, image …

Large-scale neural network (NN) accelerators typically consist of several processing nodes,
which could be implemented as a multi-or many-core chip and organized via a network-on …

Spiking Recurrent Neural Networks (SRNNs) present an alternative computing paradigm to
traditional von Neumann computing in the post-Moore's law era because they omit the …

Abstract The popularity of Machine Learning (ML) has extended to numerous disciplines,
including the domain of Network-on-chips (NoCs), leading to a consequential impact …

A future data center network is expected to be constructed by a Clos network consisting of
optical-circuit switches to deal with traffic growth. A previous model addressed a Clos …

In the last few decades, the concept of Wireless Network-on-chip (WiNoC) has emerged as a
promising alternative for Multiprocessor Systems on Chip (MPSOC) to achieve reliable and …