4 citations · 6 across the 3 of their papers we have counts for
5 papers
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…
Workload-aware Automatic Parallelization for Multi-GPU DNN Training
Sungho Shin, Youngmin Jo, Jungwook Choi +3
Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intelligence applications, but their very large and deep models impose high computational…
Single Stream Parallelization of Recurrent Neural Networks for Low Power and Fast Inference
Wonyong Sung, Jinhwan Park
As neural network algorithms show high performance in many applications, their efficient inference on mobile and embedded systems are of great interests. When a single stream recur…
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…