Real-time anomaly detection using hardware-based unsupervised spiking neural network (tinysnn)
We present TinySNN, a novel unsupervised spiking neural network hardware designed for
real-time anomaly detection. TinySNN provides an energy-efficient edge computing solution …
real-time anomaly detection. TinySNN provides an energy-efficient edge computing solution …
Noise Filtering Benchmark for Neuromorphic Satellites Observations
Event cameras capture sparse, asynchronous brightness changes which offer high temporal
resolution, high dynamic range, low power consumption, and sparse data output. These …
resolution, high dynamic range, low power consumption, and sparse data output. These …
An FPGA Implementation of An Event-Driven Unsupervised Feature Extraction Algorithm for Pattern Recognition
This paper presents the Field Programmable Gate Array (FPGA) implementation of an event-
driven unsupervised Feature Extraction using Adaptive Selection Thresholds (FEAST) …
driven unsupervised Feature Extraction using Adaptive Selection Thresholds (FEAST) …
Intrusion Detection on Industrial Networks Using Neuromorphic Computing
A Mehrabi, A Van Schaik - 2024 6th International Conference …, 2024 - ieeexplore.ieee.org
Cyber-attacks on Industrial Control Systems (ICS) present critical risks to operational
stability, public safety, and national security. As industrial networks become more integrated …
stability, public safety, and national security. As industrial networks become more integrated …
Reinforcement learning with spiking neural networks
SF CHEVTCHENKO - 2023 - bdtd.ibict.br
Artificial intelligence systems have made impressive progress in recent years, but they still
lag behind simple biological brains in terms of control capabilities and power con-sumption …
lag behind simple biological brains in terms of control capabilities and power con-sumption …