Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way. Thus, they can be used to model a variety of systems such as molecules and social ...

Graph convolutional networks have risen in popularity in recent years to tackle problems that are naturally represented as graphs. However, real-world graphs are often sparse, which means that implementing them on traditional accelerators such as ...

We present an assessment of the use of a neuromorphic camera in a Laser Warning Systems (LWS). The tested configuration is composed of a fisheye lens mounted onto a neuromorphic camera, yielding hemispherical coverage. We show that the tested ...

Radar devices sense the environment and detect range, velocity, and angel of arrival by applying multiple Fourier transformations. However, these calculations are expensive and assume that the data are in memory. Frequency-Modulated-Continuous-Wave ...

The state-of-the-art in machine learning has been achieved primarily by deep learning artificial neural networks. These networks are powerful but biologically implausible and energy intensive. In parallel, a new paradigm of neural network is being ...

We present parameter-multiplexed gradient descent (PMGD), a perturbative gradient descent framework designed to easily train emergent neuromorphic hardware platforms. We show its applicability to both analog and digital systems. We demonstrate how to ...

Traditionally, deep convolutional neural networks are computationally expensive to train and require large amounts of data samples. In this article, we explore the use of pre-trained cellular automata as a substitute for convolutional layers. We ...

Neuromorphic computing is a non-von Neumann computing paradigm that performs computation by emulating the human brain. Neuromorphic systems are extremely energy-efficient and known to consume thousands of times less power than CPUs and GPUs. They have ...

Neuromorphic vision sensors (NVS), also known as silicon retina, capture aspects of the biological functionality of the mammalian retina by transducing incident photocurrent into an asynchronous stream of spikes that denote positive and negative ...

Spiking Neural Networks are known to be very effective for neuromorphic processor implementations, achieving orders of magnitude improvements in energy efficiency and computational latency over traditional deep learning approaches. Comparable ...

In previous work, we prototyped a portable drone detection system using a DAVIS 346 event camera and a Raspberry Pi 4, running in 5.14 W. Here, we expand on this work by switching to the higher-resolution DVXplorer and by including a small neural ...

One objective of Spiking Neural Networks is a very efficient computation in terms of energy consumption. To achieve this target, a small spike rate is of course very beneficial since the event-driven nature of such a computation. However, as the network ...

Mixed-signal neuromorphic processors with brain-like organization and device physics offer an ultra-low-power alternative to the unsustainable developments of conventional deep learning and computing. However, realizing the potential of such ...

A challenge associated with effectively using spiking neuromorphic systems is how to communicate data to and from the neuromorphic implementation. Unless a neuromorphic or event-based sensing system is used, data has to be converted into spikes to be ...

Intelligent robots need to recognize objects in their environment. This task is conceptually different from the typical image classification task in computer vision. Robots need to recognize particular object instances, not classes of objects, which ...