4 citations · 7 across the 5 of their papers we have counts for
6 papers
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks
Yoonho Boo, Sungho Shin, Jungwook Choi +1
The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve t…
Quantized Neural Networks: Characterization and Holistic Optimization
Yoonho Boo, Sungho Shin, Wonyong Sung
Quantized deep neural networks (QDNNs) are necessary for low-power, high throughput, and embedded applications. Previous studies mostly focused on developing optimization methods f…
SQWA: Stochastic Quantized Weight Averaging for Improving the Generalization Capability of Low-Precision Deep Neural Networks
Sungho Shin, Yoonho Boo, Wonyong Sung
Designing a deep neural network (DNN) with good generalization capability is a complex process especially when the weights are severely quantized. Model averaging is a promising ap…
Knowledge distillation for optimization of quantized deep neural networks
Sungho Shin, Yoonho Boo, Wonyong Sung
Knowledge distillation (KD) is a very popular method for model size reduction. Recently, the technique is exploited for quantized deep neural networks (QDNNs) training as a way to…
Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations
Yoonho Boo, Wonyong Sung
Deep neural networks (DNNs) usually demand a large amount of operations for real-time inference. Especially, fully-connected layers contain a large number of weights, thus they usu…
Fixed-point optimization of deep neural networks with adaptive step size retraining
Sungho Shin, Yoonho Boo, Wonyong Sung
Fixed-point optimization of deep neural networks plays an important role in hardware based design and low-power implementations. Many deep neural networks show fairly good performa…