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02/16/2023
With Shared Microexponents, A Little Shifting Goes a Long Way
This paper introduces Block Data Representations (BDR), a framework for ...
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02/17/2020
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
We propose precision gating (PG), an end-to-end trainable dynamic dual-p...
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10/13/2019
Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators
Outliers in weights and activations pose a key challenge for fixed-point...
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01/28/2019
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Quantization can improve the execution latency and energy efficiency of ...
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01/28/2019
Improving Neural Network Quantization using Outlier Channel Splitting
Quantization can improve the execution latency and energy efficiency of ...
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11/19/2018
Building Efficient Deep Neural Networks with Unitary Group Convolutions
We propose unitary group convolutions (UGConvs), a building block for CN...
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07/15/2017
