Cerebron: A Reconfigurable Architecture for Spatio-Temporal Sparse Spiking Neural Networks

Spiking neural networks (SNNs) are promising alternatives to artificial neural networks (ANNs) since they are more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatiotemporal sparsity; thus, they are helpful in enabling energy-efficient hardware inference. However, exploiting the spatiotemporal...

Combination of traffic-responsive and gating control in urban networks: Effective interactions

Recent findings regarding macroscopic relationships of urban traffic measures such as the Macroscopic or Network Fundamental Diagram (MFD or NFD) have led to the development of novel traffic control strategies that can be applied at a network-wide level. One pertinent example is perimeter flow control (also known as gating or metering), which...