Search Results for author: Jordan Dotzel

Found 5 papers, 2 papers with code

Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design

no code implementations25 Mar 2021 Cong Hao, Jordan Dotzel, JinJun Xiong, Luca Benini, Zhiru Zhang, Deming Chen

Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives.

Edge-computing

OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators

no code implementations13 Oct 2019 Ritchie Zhao, Jordan Dotzel, Zhanqiu Hu, Preslav Ivanov, Christopher De Sa, Zhiru Zhang

Specialized hardware for handling activation outliers can enable low-precision neural networks, but at the cost of nontrivial area overhead.

Quantization

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

3 code implementations28 Jan 2019 Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang

The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training.

Language Modelling Neural Network Compression +1

Building Efficient Deep Neural Networks with Unitary Group Convolutions

no code implementations CVPR 2019 Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang

UGConvs generalize two disparate ideas in CNN architecture, channel shuffling (i. e. ShuffleNet) and block-circulant networks (i. e. CirCNN), and provide unifying insights that lead to a deeper understanding of each technique.

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