Whatmough, Paul N.

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

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

Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers

Banbury, C., Zhou, C., Fedorov, I., Navarro, R. M., Thakker, U., Gope, D., Reddi, V. J., Mattina, M., & Whatmough, P. N. (2020) · arXiv

Advanced Neural Network ApplicationsIoT and Edge/Fog Computing

Cited by 183

TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids

Fedorov, I., Stamenovic, M., Jensen, C., Yang, L.-C., Mandell, A., Gan, Y., Mattina, M., & Whatmough, P. N. (2020) · Interspeech 2020

Speech and Audio ProcessingSpeech Recognition and Synthesis

Cited by 116

Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation

Zhou, C., Kadambi, P., Mattina, M., & Whatmough, P. N. (2020) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 27

Compressing Language Models using Doped Kronecker Products

Thakker, U., Whatmough, P. N., Liu, Z.-G., Mattina, M., & Beu, J. (2020) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 7