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06/13/2022
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Data clipping is crucial in reducing noise in quantization operations an...
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12/25/2020
Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications
This paper obtains fundamental limits on the computational precision of ...
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02/22/2020
HarDNN: Feature Map Vulnerability Evaluation in CNNs
As Convolutional Neural Networks (CNNs) are increasingly being employed ...
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01/19/2019
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
Efforts to reduce the numerical precision of computations in deep learni...
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12/31/2018
Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm
The high computational and parameter complexity of neural networks makes...
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07/03/2016
Understanding the Energy and Precision Requirements for Online Learning
It is well-known that the precision of data, hyperparameters, and intern...
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07/03/2016
