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08/19/2022
Accelerating Vision Transformer Training via a Patch Sampling Schedule
We introduce the notion of a Patch Sampling Schedule (PSS), that varies ...
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02/01/2022
Accelerating DNN Training with Structured Data Gradient Pruning
Weight pruning is a technique to make Deep Neural Network (DNN) inferenc...
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10/28/2021
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
Block Floating Point (BFP) can efficiently support quantization for Deep...
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07/13/2020
Term Revealing: Furthering Quantization at Run Time on Quantized DNNs
We present a novel technique, called Term Revealing (TR), for furthering...
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05/01/2019
Full-stack Optimization for Accelerating CNNs with FPGA Validation
We present a full-stack optimization framework for accelerating inferenc...
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11/07/2018
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization
This paper describes a novel approach of packing sparse convolutional ne...
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10/21/2017
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference
We propose the use of incomplete dot products (IDP) to dynamically adjus...
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09/06/2017
Embedded Binarized Neural Networks
We study embedded Binarized Neural Networks (eBNNs) with the aim of allo...
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09/06/2017
BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
Deep neural networks are state of the art methods for many learning task...
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09/06/2017
