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06/15/2023
Retrospective: EIE: Efficient Inference Engine on Sparse and Compressed Neural Network
EIE proposed to accelerate pruned and compressed neural networks, exploi...
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07/27/2021
Griffin: Rethinking Sparse Optimization for Deep Learning Architectures
This paper examines the design space trade-offs of DNNs accelerators aim...
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01/27/2021
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
In this paper, we propose a mixed-precision convolution unit architectur...
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01/09/2020
Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators
This paper studies structured sparse training of CNNs with a gradual pru...
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01/09/2020
Campfire: Compressable, Regularization-Free, Structured Sparse Training for Hardware Accelerators
This paper studies structured sparse training of CNNs with a gradual pru...
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06/01/2017
CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks
Accelerating the inference of a trained DNN is a well studied subject. I...
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06/14/2016
A Systematic Approach to Blocking Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are the state of the art solution f...
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02/04/2016