| Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators B Reagen, PN Whatmough, R Adolf, S Rama, H Lee, SK Lee, ... International Symposium on Computer Architecture (ISCA), Proceedings of the …, 2016 | 428 | 2016 |
| A 28nm SoC with a 1.2 GHz 568nJ/prediction Sparse Deep-Neural-Network Engine with >0.1 Timing Error Rate Tolerance for IoT Applications PN Whatmough, SK Lee, H Lee, S Rama, D Brooks, GY Wei Solid-State Circuits Conference (ISSCC), 2017 IEEE International, 242-243, 2017 | 144 | 2017 |
| Ares: A framework for quantifying the resilience of deep neural networks B Reagen, U Gupta, L Pentecost, P Whatmough, SK Lee, N Mulholland, ... 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), 1-6, 2018 | 108 | 2018 |
| VLSI Architecture for a Reconfigurable Spectrally Efficient FDM Baseband Transmitter PN Whatmough, MR Perrett, S Isam, I Darwazeh IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I) 59 (5 …, 2012 | 93 | 2012 |
| VLSI Architecture for a Reconfigurable Spectrally Efficient FDM Baseband Transmitter PN Whatmough, MR Perrett, S Isam, I Darwazeh Circuits and Systems (ISCAS), 2011 IEEE International Symposium on, 1688-1691, 2011 | 93 | 2011 |
| Scale-Sim: Systolic CNN Accelerator Simulator A Samajdar, Y Zhu, P Whatmough, M Mattina, T Krishna arXiv preprint arXiv:1811.02883, 2018 | 91 | 2018 |
| Circuit-Level Timing Error Tolerance for Low-Power DSP Filters and Transforms PN Whatmough, S Das, DM Bull, I Darwazeh IEEE Transactions on Very Large Scale Integration (VLSI) Systems 21 (6), 989-999, 2013 | 65 | 2013 |
| A Low-Power 1GHz Razor FIR Accelerator with Time-Borrow Tracking Pipeline and Approximate Error Correction in 65nm CMOS PN Whatmough, S Das, DM Bull IEEE Journal of Solid-State Circuits (JSSC) 49 (1), 84-94, 2014 | 48 | 2014 |
| A Low-Power 1GHz Razor FIR Accelerator with Time-Borrow Tracking Pipeline and Approximate Error Correction in 65nm CMOS PN Whatmough, S Das, DM Bull International Solid-State Circuits Conference (ISSCC), Digest of Technical …, 2013 | 48 | 2013 |
| Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision Y Zhu, A Samajdar, M Mattina, P Whatmough 45th Annual International Symposium on Computer Architecture (ISCA), 547-560, 2018 | 47 | 2018 |
| DNN ENGINE: A 28-nm Timing-Error Tolerant Sparse Deep Neural Network Processor for IoT Applications PN Whatmough, SK Lee, D Brooks, GY Wei IEEE Journal of Solid-State Circuits (JSSC) 53 (9), 2722-2731, 2018 | 45 | 2018 |
| System-Efficiency Analysis of Power Amplifier Supply-Tracking Regimes in Mobile Transmitters BJ Minnis, PA Moore, PN Whatmough, PG Blanken, MP Van Der Heijden Circuits and Systems I: Regular Papers (TCAS-I), IEEE Transactions on 56 (1 …, 2009 | 37 | 2009 |
| A case for efficient accelerator design space exploration via bayesian optimization B Reagen, JM Hernández-Lobato, R Adolf, M Gelbart, P Whatmough, ... 2017 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2017 | 36 | 2017 |
| Modeling and Characterization of the System-Level Power Delivery Network for a Dual-Core ARM Cortex-A57 Cluster in 28nm CMOS S Das, PN Whatmough, D Bull 2015 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2015 | 34 | 2015 |
| SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers I Fedorov, RP Adams, M Mattina, P Whatmough Advances in Neural Information Processing Systems (NeurIPS), 4978-4990, 2019 | 32 | 2019 |
| Deep learning for computer architects B Reagen, R Adolf, PN Whatmough, GY Wei, D Brooks Synthesis Lectures on Computer Architecture 12 (4), 1-123, 2017 | 32 | 2017 |
| FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning PN Whatmough, C Zhou, P Hansen, SK Venkataramanaiah, J Seo, ... The 2nd Conference on Systems and Machine Learning (SysML) 2019, Palo Alto …, 2019 | 27 | 2019 |
| An All-Digital Power-Delivery Monitor for Analysis of a 28nm Dual-Core ARM Cortex-A57 Cluster PN Whatmough, S Das, Z Hadjilambrou, DM Bull International Solid-State Circuits Conference (ISSCC), 2015 IEEE, 1-3, 2015 | 23 | 2015 |
| Power Integrity Analysis of a 28 nm Dual-Core Arm Cortex-A57 Cluster using an All-Digital Power Delivery Monitor PN Whatmough, S Das, Z Hadjilambrou, DM Bull IEEE Journal of Solid-State Circuits (JSSC) 52 (6), 1643-1654, 2017 | 22 | 2017 |
| Mobile machine learning hardware at Arm: A systems-on-chip (SoC) perspective Y Zhu, M Mattina, P Whatmough 1st Conference on Systems and Machine Learning (SysML) 2019, 2018 | 19 | 2018 |
David BrooksHaley Family Professor of Computer Science, Harvard UniversityEmail verificata su eecs.harvard.edu
