Search Results for author: Yuan Wen

Found 5 papers, 0 papers with code

Building a Chinese AMR Bank with Concept and Relation Alignments

no code implementations LILT 2019 Bin Li, Yuan Wen, Li Song, Weiguang Qu, Nianwen Xue

One significant change we have made to the AMR annotation methodology is the inclusion of the alignment between word tokens in the sentence and the concepts/relations in the CAMR annotation to make it easier for automatic parsers to model the correspondence between a sentence and its meaning representation.

Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization

no code implementations22 Jun 2020 Yuan Wen, David Gregg

Pruning and quantization are proven methods for improving the performance and storage efficiency of convolutional neural networks (CNNs).

Quantization

TASO: Time and Space Optimization for Memory-Constrained DNN Inference

no code implementations21 May 2020 Yuan Wen, Andrew Anderson, Valentin Radu, Michael F. P. O'Boyle, David Gregg

We optimize the trade-off between execution time and memory consumption by: 1) attempting to minimize execution time across the whole network by selecting data layouts and primitive operations to implement each layer; and 2) allocating an appropriate workspace that reflects the upper bound of memory footprint per layer.

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

no code implementations20 Feb 2020 Valentin Radu, Kuba Kaszyk, Yuan Wen, Jack Turner, Jose Cano, Elliot J. Crowley, Bjorn Franke, Amos Storkey, Michael O'Boyle

We evaluate higher level libraries, which analyze the input characteristics of a convolutional layer, based on which they produce optimized OpenCL (Arm Compute Library and TVM) and CUDA (cuDNN) code.

Model Compression Network Pruning

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