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Found 11 papers, 3 papers with code
DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor
With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically.
"Seeing'' Electric Network Frequency from Events
Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos.
Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
This is due to the increasing number of smart devices that require sensory processing for their application on the edge.
"Seeing" Electric Network Frequency From Events
Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential to estimate the Electric Network Frequency (ENF) from conventional frame-based videos.
Synthetic Aperture Imaging With Events and Frames
This paper addresses this problem by leveraging the merits of both events and frames, leading to a fusion-based SAI (EF-SAI) that performs consistently under the different densities of occlusions.
Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence
Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications.
Scaling mixed-signal neuromorphic processors to 28 nm FD-SOI technologies
As processes continue to scale aggressively, the design of deep sub-micron, mixed-signal design is becoming more and more challenging.
Emerging Technologies
Analog circuits for mixed-signal neuromorphic computing architectures in 28 nm FD-SOI technology
Developing mixed-signal analog-digital neuromorphic circuits in advanced scaled processes poses significant design challenges.
Emerging Technologies
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle.
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements.
