| Learning a continuous and reconstructible latent space for hardware accelerator design Q Huang, C Hong, J Wawrzynek, M Subedar, YS Shao 2022 IEEE International Symposium on Performance Analysis of Systems and …, 2022 | 24 | 2022 |
| Dosa: Differentiable model-based one-loop search for dnn accelerators C Hong, Q Huang, G Dinh, M Subedar, YS Shao Proceedings of the 56th Annual IEEE/ACM International Symposium on …, 2023 | 18 | 2023 |
| A Chipyard Comparison of NVDLA and Gemmini A Gonzalez, C Hong Berkeley, CA, USA, Tech. Rep. EE290-2, 2020 | 7 | 2020 |
| Sample-Efficient Mapspace Optimization for DNN Accelerators with Bayesian Learning G Dinh, IKJ Valsala, H Luo, C Hong, Y Cho, J Demmel, S Li, Y Liu Architecture and System Support for Transformer Models (ASSYST@ ISCA 2023), 2023 | 2 | 2023 |
| Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators C Sakhuja, C Hong, C Lin arXiv preprint arXiv:2412.15548, 2024 | | 2024 |
| LLM-Aided Compilation for Tensor Accelerators C Hong, S Bhatia, A Haan, SK Dong, D Nikiforov, A Cheung, YS Shao 2024 IEEE LLM Aided Design Workshop (LAD), 1-14, 2024 | | 2024 |
| Predicting Performance of Deep Neural Network Schedules Across Accelerator Designs C Hong MICRO ACM Student Research Competition, 2021 | | 2021 |
