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06/13/2023
SqueezeLLM: Dense-and-Sparse Quantization
Generative Large Language Models (LLMs) have demonstrated remarkable res...
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02/27/2023
Full Stack Optimization of Transformer Inference: a Survey
Recent advances in state-of-the-art DNN architecture design have been mo...
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02/15/2023
Big Little Transformer Decoder
The recent emergence of Large Language Models based on the Transformer a...
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07/20/2022
BigColor: Colorization using a Generative Color Prior for Natural Images
For realistic and vivid colorization, generative priors have recently be...
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06/02/2022
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
The recently proposed Conformer model has become the de facto backbone m...
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01/23/2022
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Imperative programming allows users to implement their deep neural netwo...
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12/16/2021
A Static Analyzer for Detecting Tensor Shape Errors in Deep Neural Network Training Code
We present an automatic static analyzer PyTea that detects tensor-shape ...
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07/02/2021
Learned Token Pruning for Transformers
A major challenge in deploying transformer models is their prohibitive i...
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03/31/2021
Q-ASR: Integer-only Zero-shot Quantization for Efficient Speech Recognition
End-to-end neural network models achieve improved performance on various...
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03/25/2021
A Survey of Quantization Methods for Efficient Neural Network Inference
As soon as abstract mathematical computations were adapted to computatio...
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01/05/2021
