Neural networks have enabled great advances in recent times due mainly to improved
parallel computing capabilities in accordance to Moore's Law, which allowed reducing the …

The energy efficiency issue caused by the memory wall in traditional von Neumann
architecture is difficult to reconcile. In-memory computing (CIM) based on emerging …

Neuromorphic architectures have been introduced as platforms for energy-efficient spiking
neural network execution. The massive parallelism offered by these architectures has also …

In computational neuroscience, as well as in machine learning, neuromorphic devices
promise an accelerated and scalable alternative to neural network simulations. Their neural …

The Yin-Yang dataset was developed for research on biologically plausible error
backpropagation and deep learning in spiking neural networks. It serves as an alternative to …

Communication by binary and sparse spikes is a key factor for the energy efficiency of
biological brains. However, training deep spiking neural networks (SNNs) with …

Deep Learning algorithms have become state-of-theart methods for multiple fields, including
computer vision, speech recognition, natural language processing, and audio recognition …

The first-generation of BrainScaleS, also referred to as BrainScaleS-1, is a neuromorphic
system for emulating large-scale networks of spiking neurons. Following a'physical …

Techniques in advanced deep learning provide improvements in one or more of accuracy,
performance, and energy efficiency. An array of processing elements performs flow-based …

Techniques in advanced deep learning provide improvements in one or more of accuracy,
performance, and energy efficiency. An array of processing elements performs flow based …