Advancing neuromorphic computing with loihi: A survey of results and outlook
Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
Temporal-wise attention spiking neural networks for event streams classification
How to effectively and efficiently deal with spatio-temporal event streams, where the events
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Rectified linear postsynaptic potential function for backpropagation in deep spiking neural networks
Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit
information, which are not only biologically realistic but also suitable for ultralow-power …
information, which are not only biologically realistic but also suitable for ultralow-power …
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 …
Liaf-net: Leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing
Spiking neural networks (SNNs) based on the leaky integrate and fire (LIF) model have
been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the …
been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the …
Carsnn: An efficient spiking neural network for event-based autonomous cars on the loihi neuromorphic research processor
A Viale, A Marchisio, M Martina… - … Joint Conference on …, 2021 - ieeexplore.ieee.org
Autonomous Driving (AD) related features provide new forms of mobility that are also
beneficial for other kind of intelligent and autonomous systems like robots, smart …
beneficial for other kind of intelligent and autonomous systems like robots, smart …
Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task
Spiking Neural Networks (SNNs), known for their potential to enable low energy
consumption and computational cost, can bring significant advantages to the realm of …
consumption and computational cost, can bring significant advantages to the realm of …
NxTF: An API and compiler for deep spiking neural networks on Intel Loihi
Spiking Neural Networks (SNNs) is a promising paradigm for efficient event-driven
processing of spatio-temporally sparse data streams. Spiking Neural Networks (SNNs) have …
processing of spatio-temporally sparse data streams. Spiking Neural Networks (SNNs) have …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …