Search Results for author:
Found 4 papers, 1 papers with code
Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models
Activity and parameter sparsity are two standard methods of making neural networks computationally more efficient.
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
In this work, we demonstrate the first-ever implementation of a language model on a neuromorphic device - specifically the SpiNNaker 2 chip - based on a recently published event-based architecture called the EGRU.
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
Yet, sparse activations, while omnipresent in both biological neural networks and deep learning systems, have not been fully utilized as a compression technique in deep learning.
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
