Paper-Conference

DBC: Drift-aware Binary Code for Drift-tolerant Deep Neural Networks

Insu Choi, Jaeyong Chung, Joon-Sung Yang

Bit-slice Architecture for DNN Acceleration with Slice-level Sparsity Enhancement and Exploitation

Deep Neural Networks (DNNs) demand significant computational resources, prompting the emergence of bit-slice architectures designed to …

Insu Choi, Young-Seo Yoon, Joon-Sung Yang

ViT-slice: End-to-end Vision Transformer Accelerator with Bit-slice Algorithm

Vision Transformers have demonstrated remarkable performance in various vision tasks. However, general-purpose processors, such as CPUs …

Dong-Jin Shin, Insu Choi, Joon-Sung Yang

RQ-DNN: Reliable Quantization for Fault-tolerant Deep Neural Networks

Deep Neural Networks (DNNs) are deployed in many real-time and safety-critical applications such as autonomous vehicles and medical …

Insu Choi, Jae-Youn Hong, JaeHwa Jeon, Joon-Sung Yang

PIE-DRAM: Postponing IECC to Enhance DRAM performance with access table

This paper proposes a novel memory architecture, PIE-DRAM, to mitigate the performance overhead caused by IECC. Unlike conventional …

JaeHwa Jeon, Jae-Youn Hong, Sunghoon Kim, Insu Choi, Joon-Sung Yang

Bipolar vector classifier for fault-tolerant deep neural networks

Deep Neural Networks (DNNs) surpass the human-level performance on specific tasks. The outperforming capability accelerate an adoption …

Suyong Lee, Insu Choi, Joon-Sung Yang

Bipolar vector classifier for fault-tolerant deep neural networks