Direct training high-performance deep spiking neural networks: a review of theories and methods
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
Object detection with spiking neural networks on automotive event data
L Cordone, B Miramond… - 2022 International Joint …, 2022 - ieeexplore.ieee.org
Automotive embedded algorithms have very high constraints in terms of latency, accuracy
and power consumption. In this work, we propose to train spiking neural networks (SNNs) …
and power consumption. In this work, we propose to train spiking neural networks (SNNs) …
An electromagnetic perspective of artificial intelligence neuromorphic chips
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 …
range of complex problems. However, traditional general-purpose chips based on von …
Eas-snn: End-to-end adaptive sampling and representation for event-based detection with recurrent spiking neural networks
Event cameras, with their high dynamic range and temporal resolution, are ideally suited for
object detection in scenarios with motion blur and challenging lighting conditions. However …
object detection in scenarios with motion blur and challenging lighting conditions. However …
Direct training high-performance spiking neural networks for object recognition and detection
H Zhang, Y Li, B He, X Fan, Y Wang… - Frontiers in …, 2023 - frontiersin.org
Introduction The spiking neural network (SNN) is a bionic model that is energy-efficient
when implemented on neuromorphic hardwares. The non-differentiability of the spiking …
when implemented on neuromorphic hardwares. The non-differentiability of the spiking …
Power efficient machine learning models deployment on edge IoT devices
Computing has undergone a significant transformation over the past two decades, shifting
from a machine-based approach to a human-centric, virtually invisible service known as …
from a machine-based approach to a human-centric, virtually invisible service known as …
Neuromorphic computing for interactive robotics: a systematic review
Modelling functionalities of the brain in human-robot interaction contexts requires a real-time
understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how …
understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how …
Deep spiking residual shrinkage network for bearing fault diagnosis
Z Xu, Y Ma, Z Pan, X Zheng - IEEE Transactions on Cybernetics, 2022 - ieeexplore.ieee.org
Bearing fault diagnosis of electrical equipment has been a popular research area in recent
years because there are often some faults during continuous operation in production due to …
years because there are often some faults during continuous operation in production due to …
Spiking-diffusion: Vector quantized discrete diffusion model with spiking neural networks
Spiking neural networks (SNNs) have tremendous potential for energy-efficient
neuromorphic chips due to their binary and event-driven architecture. SNNs have been …
neuromorphic chips due to their binary and event-driven architecture. SNNs have been …
Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions
Increasing complexity and data-generation rates in cyber-physical systems and the
industrial Internet of things are calling for a corresponding increase in AI capabilities at the …
industrial Internet of things are calling for a corresponding increase in AI capabilities at the …