48 citations · 56 across the 7 of their papers we have counts for
8 papers
Scalable Smartphone Cluster for Deep Learning
Byunggook Na, Jaehee Jang, Seongsik Park +7
Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single s…
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
Seongsik Park, Seijoon Kim, Byunggook Na +1
Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their appli…
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Seijoon Kim, Seongsik Park, Byunggook Na +1
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing deman…
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
Seongsik Park, Seijoon Kim, Hyeokjun Choe +1
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability. Recently, conversion o…
Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA
Seongsik Park, Jaehee Jang, Seijoon Kim +1
Memory-augmented neural networks (MANNs) are designed for question-answering tasks. It is difficult to run a MANN effectively on accelerators designed for other neural networks (NN…
Quantized Memory-Augmented Neural Networks
Seongsik Park, Seijoon Kim, Seil Lee +2
Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural…