Dongwon Jo, Taesu Kim, Yulhwa Kim⁺, Jae-Joon Kim⁺, "Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models," Conference on Neural Information Processing Systems (NeurIPS), Dec. 2024

Jiwon Song*, Kyungseok Oh*, Taesu Kim, Hyungjun Kim, Yulhwa Kim⁺, Jae-Joon Kim⁺, "SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks," International Conference on Machine Learning (ICML), Jul. 2024.

Hyesung Jeon, Yulhwa Kim⁺, and Jae-Joon Kim⁺, "L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models," arXiv preprint arXiv:2402.04902, Feb. 2024.

Jaeyong Jang*, Yulhwa Kim*, Juheun Lee, Jae-Joon Kim⁺, "FIGNA: Integer Unit-based Accelerator Design for FP-INT GEMM Preserving Numerical Accuracy," IEEE International Symposium on High-Performance Computer Architecture (HPCA), Mar. 2024.

Yulhwa Kim, Dongwon Jo, Hyesung Jeon, Taesu Kim, Daehyun Ahn, Hyungjun Kim, Jae-Joon Kim⁺, "Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis," Conference on Neural Information Processing Systems (NeurIPS), Dec. 2023

Jiwoong Choi, Minkyu Kim, Daehyun Ahn, Taesu Kim. Yulhwa Kim, Dongwon Jo, Hyesung Jeon, Jae-Joon kim, Hyungjun Kim⁺, "Squeezing Large-Scaling Diffusion Models for Mobile," ICML 2023 Workshop on Challenges of Deploying Generative AI, Jul. 2023

Yulhwa Kim, Jaeyong Jang, Jehun Lee, Jihoon Park, Jeonghoon Kim, Byeongwook Kim, Baeseong Park, Se Jung Kwon, Dongsoo Lee, Jae-Joon kim⁺, "Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic," International Conference on Learning Representations (ICLR), May. 2023.

Yulhwa Kim, Hyungjun Kim, Jae-Joon Kim⁺, "Extreme Partial-Sum Quantization for Analog Computing-In-Memory Neural Network Accelerators," ACM Journal on Emerging Technologies in Computing Systems (JETC), vol. 18, no. 4, pp. 1-19, Oct. 2022.

Hyunmyung Oh, Hyungjun Kim, Daehyun Ahn, Jihoon Park, Yulhwa Kim, Inhwan Lee, Jae-Joon Kim⁺, "Energy-Efficient In-Memory Binary Neural Network Accelerator Design based on 8T2C SRAM Cell," IEEE Solid-State Circuits Letter (SSC-L), vol. 5, pp. 70-73, Mar. 2022.

Sungju Ryu, Hyungjun Kim, Wooseok Yi, Eunhwan Kim, Yulhwa Kim, Taesu Kim, Jae-Joon Kim⁺, "BitBlade: Energy-Efficient Variable Bit-Precision Hardware Accelerator for Quantized Neural Networks," IEEE Journal of Solid-State Circuits (JSSC), vol. 47, no. 6, pp. 1924-1935, Jun. 2022.

Hyunmyung Oh, Hyungjun Kim, Daehyun Ahn, Jihoon Park, Yulhwa Kim, Inhwan Lee, Jae-Joon Kim⁺, "Energy-Efficient Charge sharing-Based 8T2C SRAM in-Memory Accelerator for Binary Neural Networks in 28nm CMOS," IEEE Asian Solid-State Circuits Conference (A-SSCC), Nov. 2021.

Hyunmyung Oh, Hyungjun Kim, Nameun Kang, Yulhwa Kim, Jihoon Park and Jae-Joon Kim⁺, "Single RRAM Cell-Based In-Memory Accelerator Architecture for Binary Neural Networks," IEEE International Conference on Artificial Intelligence Circuits & Systems (AICAS), Jun. 2021.

Yulhwa Kim, Hyungjun Kim, Jihoon Park, Hyunmyung Oh, Jae-Joon Kim⁺, "Mapping Binary ResNets on Computing-In-Memory Hardware with Low-bit ADCs," Design, Automation and Test in Europe Conference and Exhibition (DATE), Feb. 2021.

Daehyun Ahn, Hyunmyung Oh, Hyungjun Kim, Yulhwa Kim, Jae-Joon Kim⁺, "Maximizing Parallel Activation of Word-Lines in MRAM-Based Binary Neural Network Accelerators," IEEE Access, vol. 9, pp. 141961-141969, Oct. 2021.

Wangxin He, Shihui Yin, Yulhwa Kim, Xiaoyu Sun, Jae-Joon Kim, Shimeng Yu, and Jae-sun Seo⁺, "2-Bit-per-Cell RRAM based In-Memory Computing for Area-/Energy-Efficient Deep Learning," IEEE Solid-State Circuits Letter (SSC-L), vol. 3, pp. 194-197, Jul. 2020.

Naebeom Park, Yulhwa Kim, Daehyun Ahn, Taesu Kim, Jae-Joon Kim⁺, "Time-Step Interleaved Weight Reuse for LSTM Neural Network Computing," IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), Aug. 2020.

Hyungjun Kim, Yulhwa Kim, Sungju Ryu, Jae-Joon Kim⁺, "Algorithm-Hardware Co-Design for In-Memory Neural Network Computing with Minimal Peripheral Circuit Overhead," ACM/IEEE Design Automation Conference (DAC), Jul. 2020.

Sungju Ryu, Hyungjun Kim, Wooseok Yi, Jongeun Koo, Eunhwan Kim, Yulhwa Kim, Taesu Kim, Jae-Joon Kim⁺, "A 44.1 TOPS/W Precision-Scalable Hardware Accelerator for Quantized Neural Networks in 28nm CMOS," IEEE Custom Integrated Circuits Conference (CICC), Mar. 2020.

Shihui Yin, Yandong Luo, Yulhwa Kim, Wangxin He, Xu Han, Xiaoyu Sun, Hugh Barnaby, Jae-Joon Kim, Shimeng Yu, and Jae-sun Seo⁺, "Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning," IEEE Micro, vol. 39, no. 6, pp. 54-63, Nov.-Dec. 2019.

Jinseok Kim, Jongeun Koo, Taesu Kim, Yulhwa Kim, Hyungjun Kim, Seunghyun Yoo, Jae-Joon Kim⁺, "Area-Efficient and Variation-Tolerant In-Memory BNN Computing using 6T SRAM Array," Symposium on VLSI Circuits (VLSI Symp), Jun. 2019.

Hyungjun Kim, Yulhwa Kim, Sungju Ryu, Jae-Joon Kim⁺, "BitSplit-Net: Multi-bit Deep Neural Network with Bitwise Activation Function," arXiv preprint arXiv:1903.09807, Mar. 2019.

Wooseok Yi, Yulhwa Kim, Jae-Joon Kim⁺, "Effect of Device Variation on mapping Binary Neural Network to Memristor Crossbar Array," Design Automation and Test in Europe (DATE), Mar. 2019.

Hyungjun Kim, Yulhwa Kim, Jae-Joon Kim⁺, "In-Memory Batch-Normalization for Resistive Memory based Binary Neural Network Hardware," Asian South-Pacific Design Auotmation Conference (ASP-DAC), Jan. 2019.

Yulhwa Kim, Hyungjun Kim, Jae-Joon Kim⁺, "Neural Network-Hardware Co-design for Scalable RRAM-based BNN Accelerators," arXiv preprint arXiv:1811.02187, Nov. 2018.

Jinseok Kim, Yulhwa Kim, Sungho Kim, Jae-Joon Kim⁺, "Compact Convolution Mapping on Neuromorphic Hardware using Axonal Delay," International Symposium on Low Power Electronics and Design (ISLPED), Jul. 2018.

Yulhwa Kim, Hyungjun Kim, Daehyun Ahn, Jae-Joon Kim⁺, "Input-Splitting of Large Neural Networks for Power-Efficient Accelerator with Resistive Crossbar Memory Array," International Symposium on Low Power Electronics and Design (ISLPED), Jul. 2018.