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Found 8 papers, 0 papers with code
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks
In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ).
Quantized Neural Networks: Characterization and Holistic Optimization
This study proposes a holistic approach for the optimization of QDNNs, which contains QDNN training methods as well as quantization-friendly architecture design.
SQWA: Stochastic Quantized Weight Averaging for Improving the Generalization Capability of Low-Precision Deep Neural Networks
Model averaging is a promising approach for achieving the good generalization capability of DNNs, especially when the loss surface for training contains many sharp minima.
Knowledge distillation for optimization of quantized deep neural networks
Knowledge distillation (KD) is a very popular method for model size reduction.
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices
The RNN implementation on embedded devices can suffer from excessive DRAM accesses because the parameter size of a neural network usually exceeds that of the cache memory and the parameters are used only once for each time step.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks
The approximate probability of each word can be estimated with only a small part of the weight matrix by using a few large singular values and the corresponding elements for most of the words.
Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations
Deep neural networks (DNNs) usually demand a large amount of operations for real-time inference.
Fixed-point optimization of deep neural networks with adaptive step size retraining
Fixed-point optimization of deep neural networks plays an important role in hardware based design and low-power implementations.
