Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

The emergence of artificial intelligence has represented great potential in solving a wide
range of complex problems. However, traditional general-purpose chips based on von …

This paper investigates the power and performance trade-offs associated with integrating
deep neural network (DNN) computation in an image sensor. The paper presents the design …

Neuromorphic systems have shown improvements over the years, leveraging Spiking neural
networks (SNN) event-driven nature to demonstrate low power consumption. As …

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 …

The traditional neural network Intelligent chip has the problem of high power consumption
due to classical computing architecture, limiting the development of neural network …

Image Sensors will get more and more pervasive into their environment. In the context of
Automotive and IoT, low cost image sensors, with high quality pixels, will embed more and …

In this paper, we proposed to use 3D integration technology to create a neuromorphic
hardware system that is compatible with current technology, provides high system speed …