| Fidelity: Efficient resilience analysis framework for deep learning accelerators Y He, P Balaprakash, Y Li 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture …, 2020 | 57 | 2020 |
| Special session: on the reliability of conventional and quantum neural network hardware M Sadi, Y He, Y Li, M Alam, S Kundu, S Ghosh, J Bahrami, N Karimi 2022 IEEE 40th VLSI Test Symposium (VTS), 1-12, 2022 | 27 | 2022 |
| Efficient functional in-field self-test for deep learning accelerators Y He, T Uezono, Y Li 2021 IEEE International Test Conference (ITC), 93-102, 2021 | 17 | 2021 |
| Understanding and mitigating hardware failures in deep learning training systems Y He, M Hutton, S Chan, R De Gruijl, R Govindaraju, N Patil, Y Li Proceedings of the 50th Annual International Symposium on Computer …, 2023 | 8 | 2023 |
| Achieving automotive safety requirements through functional in-field self-test for deep learning accelerators T Uezono, Y He, Y Li 2022 IEEE International Test Conference (ITC), 465-473, 2022 | 5 | 2022 |
| Time-slicing soft error resilience in microprocessors for reliable and energy-efficient execution Y He, Y Li 2019 IEEE International Test Conference (ITC), 1-10, 2019 | 3 | 2019 |
| Understanding Permanent Hardware Failures in Deep Learning Training Accelerator Systems Y He, Y Li 2023 IEEE European Test Symposium (ETS), 1-6, 2023 | 1 | 2023 |
| Resilient Deep Learning Accelerators Y He The University of Chicago, 2023 | | 2023 |
