Donghyeon Han

According to our database1, Donghyeon Han authored at least 17 papers between 2018 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

On csauthors.net:

Bibliography

2020
A 1.15 TOPS/W Energy-Efficient Capsule Network Accelerator for Real-Time 3D Point Cloud Segmentation in Mobile Environment.
IEEE Trans. Circuits Syst. II Express Briefs, 2020

The Hardware and Algorithm Co-Design for Energy-Efficient DNN Processor on Edge/Mobile Devices.
IEEE Trans. Circuits Syst., 2020

A 0.22-0.89 mW Low-Power and Highly-Secure Always-On Face Recognition Processor With Adversarial Attack Prevention.
IEEE Trans. Circuits Syst. II Express Briefs, 2020

DT-CNN: An Energy-Efficient Dilated and Transposed Convolutional Neural Network Processor for Region of Interest Based Image Segmentation.
IEEE Trans. Circuits Syst., 2020

Extension of Direct Feedback Alignment to Convolutional and Recurrent Neural Network for Bio-plausible Deep Learning.
CoRR, 2020

A 4.45 ms Low-Latency 3D Point-Cloud-Based Neural Network Processor for Hand Pose Estimation in Immersive Wearable Devices.
Proceedings of the IEEE Symposium on VLSI Circuits, 2020

7.4 GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation.
Proceedings of the 2020 IEEE International Solid- State Circuits Conference, 2020

2019
A Low-Power Deep Neural Network Online Learning Processor for Real-Time Object Tracking Application.
IEEE Trans. Circuits Syst. I Regul. Pap., 2019

CNNP-v2: A Memory-Centric Architecture for Low-Power CNN Processor on Domain-Specific Mobile Devices.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2019

Efficient Convolutional Neural Network Training with Direct Feedback Alignment.
CoRR, 2019

A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture.
Proceedings of the 2019 Symposium on VLSI Circuits, Kyoto, Japan, June 9-14, 2019, 2019

LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16.
Proceedings of the IEEE International Solid- State Circuits Conference, 2019

DT-CNN: Dilated and Transposed Convolution Neural Network Accelerator for Real-Time Image Segmentation on Mobile Devices.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2019

Direct Feedback Alignment Based Convolutional Neural Network Training for Low-Power Online Learning Processor.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

CNNP-v2: An Energy Efficient Memory-Centric Convolutional Neural Network Processor Architecture.
Proceedings of the IEEE International Conference on Artificial Intelligence Circuits and Systems, 2019

2018
A Low-Power Convolutional Neural Network Face Recognition Processor and a CIS Integrated With Always-on Face Detector.
IEEE J. Solid State Circuits, 2018

A 141.4 mW Low-Power Online Deep Neural Network Training Processor for Real-time Object Tracking in Mobile Devices.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2018


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