Analyzing OpenCL 2.0 workloads using a heterogeneous CPU-GPU simulator. - Li Wang; Ren-Wei Tsai; Shao-Chung Wang; Kun-Chih Chen; Po-Han Wang 001; Hsiang-Yun Cheng; Yi-Chung Lee; Sheng-Jie Shu; Chun-Chieh Yang; Min-Yih Hsu; Li-Chen Kan; Chao-Lin Lee; et al. (ISPASS, 2017)

Enable the Flow for GPGPU-Sim Simulators with Fixed-Point Instructions. - Chao-Lin Lee; Min-Yih Hsu; Bing-Sung Lu; Jenq-Kuen Lee (ICPP Workshops, 2018)

Architecture and Compiler Support for GPUs Using Energy-Efficient Affine Register Files. - Shao-Chung Wang; Li-Chen Kan; Chao-Lin Lee; Yuan-Shin Hwang; Jenq-Kuen Lee (ACM Trans. Design Autom. Electr. Syst., 2018)

Accelerate DNN Performance with Sparse Matrix Compression in Halide. - Chao-Lin Lee; Chen-Ting Chao; Jenq-Kuen Lee; Ming-Yu Hung; Chung-Wen Huang (ICPP Workshops, 2019)

Sparse-Matrix Compression Primitives with OpenCL Framework to Support Halide. - Chao-Lin Lee; Chen-Ting Chao; Jenq-Kuen Lee; Chung-Wen Huang; Ming-Yu Hung (IWOCL, 2019)

Devise Sparse Compression Schedulers to Enhance FastText Methods. - Chen-Ting Chao; Wei-Hsu Chu; Chao-Lin Lee; Jenq-Kuen Lee; Ming-Yu Hung; Hsiang-Wei Sung (ICPP Workshops, 2020)

Experiments and optimizations for TVM on RISC-V Architectures with P Extension. - Yi-Ru Chen; Hui-Hsin Liao; Chia-Hsuan Chang; Che-Chia Lin; Chao-Lin Lee; Yuan-Ming Chang; Chun-Chieh Yang; Jenq-Kuen Lee (VLSI-DAT, 2020)

Experiment and enabled flow for GPGPU-Sim simulators with fixed-point instructions. - Chao-Lin Lee; Min-Yih Hsu; Bing-Sung Lu; Ming-Yu Hung; Jenq-Kuen Lee (J. Syst. Archit., 2020)

Support Convolution of CNN with Compression Sparse Matrix Multiplication Flow in TVM. - Hui-Hsin Liao; Chao-Lin Lee; Jenq-Kuen Lee; Wei-Chih Lai; Ming-Yu Hung; Chung-Wen Huang (ICPP Workshops, 2021)

Accelerate Binarized Neural Networks with Processing-in-Memory Enabled by RISC-V Custom Instructions. - Che-Chia Lin; Chao-Lin Lee; Jenq-Kuen Lee; Howard Wang; Ming-Yu Hung (ICPP Workshops, 2021)

Register-Pressure Aware Predicator for Length Multiplier of RVV. - Meng-Shiuan Shih; Hung-Ming Lai; Chao-Lin Lee; Chung-Kai Chen; Jenq-Kuen Lee (ICPP Workshops, 2022)

Efficient Realization of Decision Trees for Real-Time Inference. - Kuan-Hsun Chen; Chiahui Su; Christian Hakert; Sebastian Buschjäger; Chao-Lin Lee; Jenq-Kuen Lee; Katharina Morik; Jian-Jia Chen (ACM Trans. Embed. Comput. Syst., 2022)

Support of Sparse Tensor Computing for MLIR HLS. - Geng-Ming Liang; Chao-Lin Lee; Robert Lai; Jenq-Kuen Lee (ICPP Workshops, 2023)

SIMD Everywhere Optimization from ARM NEON to RISC-V Vector Extensions. - Ju-Hung Li; Jhih-Kuan Lin; Yung-Cheng Su; Chi-Wei Chu; Lai-Tak Kuok; Hung-Ming Lai; Chao-Lin Lee; Jenq-Kuen Lee (CoRR, 2023)

Accelerating AI Applications with Sparse Matrix Compression in Halide. - Chao-Lin Lee; Chen-Ting Chao; Wei-Hsu Chu; Ming-Yu Hung; Jenq-Kuen Lee (J. Signal Process. Syst., 2023)

Accelerating AI performance with the incorporation of TVM and MediaTek NeuroPilot. - Chao-Lin Lee; Chun-Ping Chung; Sheng-Yuan Cheng; Jenq-Kuen Lee; Robert Lai (Connect. Sci., 2023)

The Rewriting of DataRaceBench Benchmark for OpenCL Program Validations. - Chia-Chen Hsu; Chun-Lin Huang; Chao-Lin Lee; Jenq-Kuen Lee; Pei-Hung Lin (ICPP Workshops, 2024)

Low DRAM Memory Access and Flexible Dataflow Convolutional Neural Network Accelerator based on RISC-V Custom Instruction. - Yi-Fan Chen; Yu-Jen Chang; Ching-Te Chiu; Ming-Long Huang; Geng-Ming Liang; Chao-Lin Lee; Jenq-Kuen Lee; Ping-Yu Hsieh; Wei-Chih Lai (ISCAS, 2024)