no code implementations • 29 Dec 2022 • Chanwoo Kim, Sathish Indurti, Jinhwan Park, Wonyong Sung
In our work, we define a macro-block that contains a large number of units from the input to a Recurrent Neural Network (RNN).
no code implementations • 5 Sep 2020 • Wonyong Sung, Iksoo Choi, Jinhwan Park, Seokhyun Choi, Sungho Shin
The proposed method is compared with the conventional SGD method and previous weight-noise injection algorithms using convolutional neural networks for image classification.
no code implementations • NeurIPS 2018 • Jinhwan Park, Yoonho Boo, Iksoo Choi, Sungho Shin, Wonyong Sung
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) +1
no code implementations • 30 Mar 2018 • 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.
no code implementations • 30 Sep 2016 • Minjae Lee, Kyuyeon Hwang, Jinhwan Park, Sungwook Choi, Sungho Shin, Wonyong Sung
The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA.
no code implementations • 4 Feb 2016 • Jinhwan Park, Wonyong Sung
In this work, we have developed an FPGA based fixed-point DNN system using only on-chip memory not to access external DRAM.