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10/08/2022
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
There are growing interests in adapting large-scale language models usin...
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09/22/2022
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation
Transformer is a deep learning language model widely used for natural la...
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06/20/2022
nuQmm: Quantized MatMul for Efficient Inference of Large-Scale Generative Language Models
The recent advance of self-supervised learning associated with the Trans...
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05/27/2022
Maximum Likelihood Training of Implicit Nonlinear Diffusion Models
Whereas diverse variations of diffusion models exist, expanding the line...
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05/05/2021
Modulating Regularization Frequency for Efficient Compression-Aware Model Training
While model compression is increasingly important because of large neura...
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05/05/2021
Sequential Encryption of Sparse Neural Networks Toward Optimum Representation of Irregular Sparsity
Even though fine-grained pruning techniques achieve a high compression r...
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05/05/2021
Q-Rater: Non-Convex Optimization for Post-Training Uniform Quantization
Various post-training uniform quantization methods have usually been stu...
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12/14/2020
A review of on-device fully neural end-to-end automatic speech recognition algorithms
In this paper, we review various end-to-end automatic speech recognition...
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09/16/2020
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation
Transformer is being widely used in Neural Machine Translation (NMT). De...
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09/09/2020
FleXOR: Trainable Fractional Quantization
Quantization based on the binary codes is gaining attention because each...
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05/20/2020
BiQGEMM: Matrix Multiplication with Lookup Table For Binary-Coding-based Quantized DNNs
The number of parameters in deep neural networks (DNNs) is rapidly incre...
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05/24/2019
Learning Low-Rank Approximation for CNNs
Low-rank approximation is an effective model compression technique to no...
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05/24/2019
Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks
Model compression techniques, such as pruning and quantization, are beco...
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05/14/2019
Network Pruning for Low-Rank Binary Indexing
Pruning is an efficient model compression technique to remove redundancy...
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10/30/2018
DeepTwist: Learning Model Compression via Occasional Weight Distortion
Model compression has been introduced to reduce the required hardware re...
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05/29/2018
