13 citations · 29 across the 5 of their papers we have counts for
5 papers
Energy-efficient Dense DNN Acceleration with Signed Bit-slice Architecture
Dongseok Im, Gwangtae Park, Zhiyong Li +2
As the number of deep neural networks (DNNs) to be executed on a mobile system-on-chip (SoC) increases, the mobile SoC suffers from the real-time DNN acceleration within its limite…
Two-Step Spike Encoding Scheme and Architecture for Highly Sparse Spiking-Neural-Network
Sangyeob Kim, Sangjin Kim, Soyeon Um +2
This paper proposes a two-step spike encoding scheme, which consists of the source encoding and the process encoding for a high energy-efficient spiking-neural-network (SNN) accele…
GST: Group-Sparse Training for Accelerating Deep Reinforcement Learning
Juhyoung Lee, Sangyeob Kim, Sangjin Kim +2
Deep reinforcement learning (DRL) has shown remarkable success in sequential decision-making problems but suffers from a long training time to obtain such good performance. Many pa…
Extension of Direct Feedback Alignment to Convolutional and Recurrent Neural Network for Bio-plausible Deep Learning
Donghyeon Han, Gwangtae Park, Junha Ryu +1
Throughout this paper, we focus on the improvement of the direct feedback alignment (DFA) algorithm and extend the usage of the DFA to convolutional and recurrent neural networks (…
Efficient Convolutional Neural Network Training with Direct Feedback Alignment
Donghyeon Han, Hoi-jun Yoo
There were many algorithms to substitute the back-propagation (BP) in the deep neural network (DNN) training. However, they could not become popular because their training accuracy…