Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

Spiking neural networks are efficient computation models for low-power environments.
Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful …

The high energy cost of processing deep convolutional neural networks impedes their
ubiquitous deployment in energy-constrained platforms such as embedded systems and IoT …

Data in terms of term have filled the daily activities of human life. This term becomes
important, when humans are increasingly dependent on technology called computers or …

The structure of the human brain has evolved to achieve extraordinary computing power
through continuous refinement by natural selection. At present, the topology of brain-like …

Spiking neural networks are efficient computation models for low-power environments.
Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful …

We present a processing-in-memory (PIM)-based hardware platform, referred to as
MONETA, for on-chip acceleration of inference and learning in hybrid convolutional spiking …

Data dari sisi istilah telah mengisi aktivitas sehari-hari dari kehidupan manusia. Istilah ini
menjadi penting, ketika manusia semakin tergantung terhadap teknologi bernama komputer …

Among the various training algorithms for spiking neural network (SNN), ANN-to-SNN
conversion gained popularity due to high accuracy and scalability to deep networks. By …

En las últimas décadas, la detección de objetos ha sido una tarea muy importante en el
área de visión por computadora, ya que la detección de objetos localiza y clasifica uno o …