Search Results for author: Sehoon Kim

Found 14 papers, 10 papers with code

Full Stack Optimization of Transformer Inference: a Survey

no code implementations27 Feb 2023 Sehoon Kim, Coleman Hooper, Thanakul Wattanawong, Minwoo Kang, Ruohan Yan, Hasan Genc, Grace Dinh, Qijing Huang, Kurt Keutzer, Michael W. Mahoney, Yakun Sophia Shao, Amir Gholami

In this work, we survey different approaches for efficient Transformer inference, including: (i) analysis and profiling of the bottlenecks in existing Transformer architectures and their similarities and differences with previous convolutional models; (ii) implications of Transformer architecture on hardware, including the impact of non-linear operations such as Layer Normalization, Softmax, and GELU, as well as linear operations, on hardware design; (iii) approaches for optimizing a fixed Transformer architecture; (iv) challenges in finding the right mapping and scheduling of operations for Transformer models; and (v) approaches for optimizing Transformer models by adapting the architecture using neural architecture search.

Neural Architecture Search Scheduling

Big Little Transformer Decoder

1 code implementation15 Feb 2023 Sehoon Kim, Karttikeya Mangalam, Suhong Moon, John Canny, Jitendra Malik, Michael W. Mahoney, Amir Gholami, Kurt Keutzer

To address this, we propose Big Little Decoder (BiLD), a framework that can improve inference efficiency and latency for a wide range of text generation applications.

Language Modelling Machine Translation +1

BigColor: Colorization using a Generative Color Prior for Natural Images

1 code implementation20 Jul 2022 Geonung Kim, Kyoungkook Kang, Seongtae Kim, Hwayoon Lee, Sehoon Kim, Jonghyun Kim, Seung-Hwan Baek, Sunghyun Cho

In this paper, we propose BigColor, a novel colorization approach that provides vivid colorization for diverse in-the-wild images with complex structures.


Squeezeformer: An Efficient Transformer for Automatic Speech Recognition

3 code implementations2 Jun 2022 Sehoon Kim, Amir Gholami, Albert Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W. Mahoney, Kurt Keutzer

After re-examining the design choices for both the macro and micro-architecture of Conformer, we propose Squeezeformer which consistently outperforms the state-of-the-art ASR models under the same training schemes.

Automatic Speech Recognition Automatic Speech Recognition (ASR)

A Fast Post-Training Pruning Framework for Transformers

1 code implementation29 Mar 2022 Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, Amir Gholami

To address this, we propose a fast post-training pruning framework for Transformers that does not require any retraining.

Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs

no code implementations NeurIPS 2021 Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun

Recently, several systems have been proposed to combine the usability of imperative programming with the optimized performance of symbolic graph execution.

A Static Analyzer for Detecting Tensor Shape Errors in Deep Neural Network Training Code

1 code implementation16 Dec 2021 Ho Young Jhoo, Sehoon Kim, Woosung Song, Kyuyeon Park, DongKwon Lee, Kwangkeun Yi

We present an automatic static analyzer PyTea that detects tensor-shape errors in PyTorch code.

Applications and Techniques for Fast Machine Learning in Science

no code implementations25 Oct 2021 Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

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 experimental data processing loop to accelerate scientific discovery.

BIG-bench Machine Learning

Learned Token Pruning for Transformers

1 code implementation2 Jul 2021 Sehoon Kim, Sheng Shen, David Thorsley, Amir Gholami, Woosuk Kwon, Joseph Hassoun, Kurt Keutzer

We extensively test the performance of LTP on GLUE tasks and show that our method outperforms the prior state-of-the-art token pruning methods by up to ~2. 5% higher accuracy with the same amount of FLOPs.

A Survey of Quantization Methods for Efficient Neural Network Inference

no code implementations25 Mar 2021 Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer

Thus, it is not surprising that quantization has emerged recently as an important and very active sub-area of research in the efficient implementation of computations associated with Neural Networks.

Efficient Neural Network Quantization

Hessian-Aware Pruning and Optimal Neural Implant

1 code implementation22 Jan 2021 Shixing Yu, Zhewei Yao, Amir Gholami, Zhen Dong, Sehoon Kim, Michael W Mahoney, Kurt Keutzer

To address this problem, we introduce a new Hessian Aware Pruning (HAP) method coupled with a Neural Implant approach that uses second-order sensitivity as a metric for structured pruning.

I-BERT: Integer-only BERT Quantization

3 code implementations5 Jan 2021 Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer

Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks.

Natural Language Inference Natural Language Understanding +1

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach

1 code implementation10 Jun 2019 Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation.

BIG-bench Machine Learning Translation

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