Graph neural networks (GNNs) have emerged as a powerful approach for
mod...
Transformer neural networks are rapidly being integrated into
state-of-t...
Emerging AI applications such as ChatGPT, graph convolutional networks, ...
Domain-specific machine learning (ML) accelerators such as Google's TPU ...
Recurrent Neural Networks (RNNs) are used in applications that learn
dep...
Parameter quantization in convolutional neural networks (CNNs) can help
...
Sparse neural networks can greatly facilitate the deployment of neural
n...
The approximate computing paradigm advocates for relaxing accuracy goals...
Domain-specific neural network accelerators have seen growing interest i...
The approximate computing paradigm advocates for relaxing accuracy goals...