With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …

Neuromorphic hardware enables fast and power-efficient neural network–based artificial
intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …

The increasing demand for on-chip edge intelligence has motivated the exploration of
algorithmic techniques and specialized hardware to reduce the computing energy of current …

Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …

This paper argues that the field of artificial intelligence (AI) ethics needs to give more
attention to the values and interests of nonhumans such as other biological species and the …

The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated
growing interest in understanding their environmental impact and the challenges associated …

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence.
However, training deep SNNs from scratch or converting deep artificial neural networks to …

One of the essential functions of biological neural networks is the processing of information.
This includes everything from processing sensory information to perceive the environment …

Spiking Neural Networks (SNNs) is a promising paradigm for efficient event-driven
processing of spatio-temporally sparse data streams. Spiking Neural Networks (SNNs) have …