77 citations · 349 across the 17 of their papers we have counts for
6 papers · 1 filter
A Static Analyzer for Detecting Tensor Shape Errors in Deep Neural Network Training Code
Ho Young Jhoo, Sehoon Kim, Woosung Song +3
We present an automatic static analyzer PyTea that detects tensor-shape errors in PyTorch code. The tensor-shape error is critical in the deep neural net code; much of the training…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
Learned Token Pruning for Transformers
Sehoon Kim, Sheng Shen, David Thorsley +4
Deploying transformer models in practice is challenging due to their inference cost, which scales quadratically with input sequence length. To address this, we present a novel Lear…
Integer-only Zero-shot Quantization for Efficient Speech Recognition
Sehoon Kim, Amir Gholami, Zhewei Yao +7
End-to-end neural network models achieve improved performance on various automatic speech recognition (ASR) tasks. However, these models perform poorly on edge hardware due to larg…
A Survey of Quantization Methods for Efficient Neural Network Inference
Amir Gholami, Sehoon Kim, Zhen Dong +3
As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the nume…
I-BERT: Integer-only BERT Quantization
Sehoon Kim, Amir Gholami, Zhewei Yao +2
Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks. However, their memory footprint, inference latenc…