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2020 – today
- 2024
[i3]Taesu Kim, Jongho Lee, Daehyun Ahn, Sarang Kim, Jiwoong Choi, Minkyu Kim, Hyungjun Kim:
QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference. CoRR abs/2402.10076 (2024)- 2023
- [j3]Taesu Kim, Daehyun Ahn
, Dongsoo Lee, Jae-Joon Kim:
V-LSTM: An Efficient LSTM Accelerator Using Fixed Nonzero-Ratio Viterbi-Based Pruning. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(10): 3327-3337 (2023) - [c12]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. NeurIPS 2023 - [c11]Junhyuk So, Jungwon Lee, Daehyun Ahn, Hyungjun Kim, Eunhyeok Park:
Temporal Dynamic Quantization for Diffusion Models. NeurIPS 2023 - [c10]Daehyun Ahn, Hyungjun Kim, Taesu Kim, Eunhyeok Park, Jae-Joon Kim:
Searching for Robust Binary Neural Networks via Bimodal Parameter Perturbation. WACV 2023: 2409-2418 - [i2]Junhyuk So, Jungwon Lee, Daehyun Ahn, Hyungjun Kim, Eunhyeok Park:
Temporal Dynamic Quantization for Diffusion Models. CoRR abs/2306.02316 (2023) - [i1]Jiwoong Choi, Minkyu Kim, Daehyun Ahn, Taesu Kim, Yulhwa Kim, Dongwon Jo, Hyesung Jeon, Jae-Joon Kim, Hyungjun Kim:
Squeezing Large-Scale Diffusion Models for Mobile. CoRR abs/2307.01193 (2023) - 2022
- [c9]Naebeom Park, Daehyun Ahn, Jae-Joon Kim:
Workload-Balanced Graph Attention Network Accelerator with Top-K Aggregation Candidates. ICCAD 2022: 39:1-39:9 - 2021
- [j2]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 9: 141961-141969 (2021) - [c8]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. A-SSCC 2021: 1-3 - [c7]Sungju Ryu, Youngtaek Oh, Taesu Kim, Daehyun Ahn, Jae-Joon Kim:
SPRITE: Sparsity-Aware Neural Processing Unit with Constant Probability of Index-Matching. DATE 2021: 663-666 - 2020
- [j1]Junki Park, Wooseok Yi, Daehyun Ahn, Jaeha Kung, Jae-Joon Kim:
Balancing Computation Loads and Optimizing Input Vector Loading in LSTM Accelerators. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39(9): 1889-1901 (2020) - [c6]Taesu Kim, Daehyun Ahn, Jae-Joon Kim:
V-LSTM: An Efficient LSTM Accelerator Using Fixed Nonzero-Ratio Viterbi-Based Pruning. FPGA 2020: 326 - [c5]Naebeom Park, Yulhwa Kim, Daehyun Ahn, Taesu Kim, Jae-Joon Kim:
Time-step interleaved weight reuse for LSTM neural network computing. ISLPED 2020: 13-18 - [c4]Junki Park, Hyunsung Yoon, Daehyun Ahn, Jungwook Choi, Jae-Joon Kim:
OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator. MLSys 2020
2010 – 2019
- 2019
- [c3]Daehyun Ahn, Dongsoo Lee, Taesu Kim, Jae-Joon Kim:
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network. ICLR (Poster) 2019 - 2018
- [c2]Dongsoo Lee, Daehyun Ahn, Taesu Kim, Pierce I-Jen Chuang, Jae-Joon Kim:
Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio. ICLR (Poster) 2018 - [c1]Yulhwa Kim, Hyungjun Kim, Daehyun Ahn, Jae-Joon Kim:
Input-Splitting of Large Neural Networks for Power-Efficient Accelerator with Resistive Crossbar Memory Array. ISLPED 2018: 41:1-41:6
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