145 citations · 162 across the 7 of their papers we have counts for
7 papers
Neuromorphic architectures as numerical solvers for computational neuroscience
Jakob Jordan, Ole Richter, Congyang Li +2
Neuromorphic computing is closely associated with spiking neuronal networks. However, an alternative class of so-called "rate-based" models arising from computational neuroscience…
An Asynchronous Mixed-Signal Resonate-and-Fire Neuron
Giuseppe Leo, Paolo Gibertini, Irem Ilter +3
Analog computing at the edge is an emerging strategy to limit data storage and transmission requirements, as well as energy consumption, and its practical implementation is in its…
A scalable event-driven spatiotemporal feature extraction circuit
Hugh Greatorex, Michele Mastella, Ole Richter +4
Event-driven sensors, which produce data only when there is a change in the input signal, are increasingly used in applications that require low-latency and low-power real-time sen…
Event-based vision for egomotion estimation using precise event timing
Hugh Greatorex, Michele Mastella, Madison Cotteret +2
Egomotion estimation is crucial for applications such as autonomous navigation and robotics, where accurate and real-time motion tracking is required. However, traditional methods…
DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor
Ole Richter, Chenxi Wu, Adrian M. Whatley +4
With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these ap…
Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
Ole Richter, Yannan Xing, Michele De Marchi +9
Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smar…
Ferroelectric Tunneling Junctions for Edge Computing
Erika Covi, Quang T. Duong, Suzanne Lancaster +10
Ferroelectric tunneling junctions (FTJ) are considered to be the intrinsically most energy efficient memristors. In this work, specific electrical features of ferroelectric hafnium…