Wei, Gu-Yeon

Minerva: enabling low-power, highly-accurate deep neural network accelerators

Reagen, B., Whatmough, P., Adolf, R., Rama, S., Lee, H., Lee, S. K., Hernández-Lobato, J. M., Wei, G.-Y., & Brooks, D. (2016) · ACM SIGARCH Computer Architecture News

Low-power high-performance VLSI designAdvanced Neural Network Applications

Cited by 446

MLPerf Training Benchmark

Mattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., Tang, H., Wei, G.-Y., Bailis, P., Bittorf, V., et al. (2019) · arXiv

Parallel Computing and Optimization TechniquesCloud Computing and Resource Management

Cited by 206

Ares: A framework for quantifying the resilience of deep neural networks

Reagen, B., Gupta, U., Pentecost, L., Whatmough, P., Lee, S. K., Mulholland, N., Brooks, D., & Wei, G.-Y. (2018) · 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)

Distributed systems and fault toleranceRadiation Effects in Electronics

Cited by 189

MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance

Mattson, P., Reddi, V. J., Cheng, C., Coleman, C., Diamos, G., Kanter, D., Micikevicius, P., Patterson, D., Schmuelling, G., Tang, H., et al. (2020) · IEEE Micro

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 171

$S^3$: Increasing GPU Utilization during Generative Inference for Higher Throughput

Jin, Y., Wu, C.-F., Brooks, D., & Wei, G.-Y. (2023) · Advances in Neural Information Processing Systems 36

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 20

The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute

Jin, Y., Wei, G.-Y., & Brooks, D. (2025) · arXiv

Topic ModelingGreen IT and Sustainability

Cited by 0