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Found 7 papers, 3 papers with code
An Asynchronous Multi-core Accelerator for SNN inference
In this paper, we propose an asynchronous architecture for Spiking Neural Networks (SNNs) that eliminates the need for inter-core synchronization, thus enhancing speed and energy efficiency.
EAS-SNN: End-to-End Adaptive Sampling and Representation for Event-based Detection with Recurrent Spiking Neural Networks
Spiking Neural Networks (SNNs), which operate on an event-driven paradigm through sparse spike communication, emerge as a natural fit for addressing this challenge.
Learning to Manipulate Artistic Images
Recent advancement in computer vision has significantly lowered the barriers to artistic creation.
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning
The Darwin3 chip supports up to 2. 35 million neurons, making it the largest of its kind in neuron scale.
Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption.
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an SNN.
Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes
Cultural heritage is the asset of all the peoples of the world.
