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2020 – today
- 2024
[c52]Abhishek Tyagi, Reiley Jeyapaul, Chuteng Zhou, Paul N. Whatmough, Yuhao Zhu:
Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator. ISPASS 2024: 96-108- [i35]Mart van Baalen, Andrey Kuzmin, Markus Nagel, Peter Couperus, Cédric Bastoul, Eric Mahurin, Tijmen Blankevoort, Paul N. Whatmough:
GPTVQ: The Blessing of Dimensionality for LLM Quantization. CoRR abs/2402.15319 (2024) - [i34]Abhishek Tyagi, Reiley Jeyapaul, Chuteng Zhu, Paul N. Whatmough, Yuhao Zhu:
Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator. CoRR abs/2404.09317 (2024) - [i33]Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi, Viswanath Ganapathy, Rafael Esteves, Shreya Kadambi, Shubhankar Borse, Paul N. Whatmough, Risheek Garrepalli, Mart van Baalen, Harris Teague, Markus Nagel:
Sparse High Rank Adapters. CoRR abs/2406.13175 (2024) - [i32]Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi, Viswanath Ganapathy, Rafael Esteves, Shreya Kadambi, Shubhankar Borse, Paul N. Whatmough, Risheek Garrepalli, Mart van Baalen, Harris Teague, Markus Nagel:
Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters. CoRR abs/2407.16712 (2024) - 2023
- [j18]Thierry Tambe
, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei:
A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs. IEEE J. Solid State Circuits 58(2): 569-581 (2023) - [c51]Anil Kag, Igor Fedorov, Aditya Gangrade, Paul N. Whatmough, Venkatesh Saligrama:
Efficient Edge Inference by Selective Query. ICLR 2023 - [c50]Teyuh Chou, Fernando García-Redondo, Paul N. Whatmough, Zhengya Zhang:
AR-PIM: An Adaptive-Range Processing-in-Memory Architecture. ISLPED 2023: 1-6 - [c49]Thierry Tambe, Jeff Zhang, Coleman Hooper, Tianyu Jia, Paul N. Whatmough, Joseph Zuckerman, Maico Cassel dos Santos, Erik Jens Loscalzo, Davide Giri, Kenneth L. Shepard, Luca P. Carloni, Alexander M. Rush, David Brooks, Gu-Yeon Wei:
A 12nm 18.1TFLOPs/W Sparse Transformer Processor with Entropy-Based Early Exit, Mixed-Precision Predication and Fine-Grained Power Management. ISSCC 2023: 342-343 - [c48]Yu Feng, Patrick Hansen, Paul N. Whatmough, Guoyu Lu, Yuhao Zhu:
Fast and Accurate: Video Enhancement Using Sparse Depth. WACV 2023: 4481-4489 - [i31]Yuji Chai, Devashree Tripathy, Chuteng Zhou, Dibakar Gope, Igor Fedorov, Ramon Matas Navarro, David Brooks, Gu-Yeon Wei, Paul N. Whatmough:
PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices. CoRR abs/2301.10999 (2023) - 2022
- [j17]Sae Kyu Lee, Paul N. Whatmough, Marco Donato, Glenn G. Ko, David Brooks, Gu-Yeon Wei:
SMIV: A 16-nm 25-mm² SoC for IoT With Arm Cortex-A53, eFPGA, and Coherent Accelerators. IEEE J. Solid State Circuits 57(2): 639-650 (2022) - [j16]Chuteng Zhou, Fernando García-Redondo, Julian Büchel, Irem Boybat, Xavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian, Manuel Le Gallo, Paul N. Whatmough:
ML-HW Co-Design of Noise-Robust TinyML Models and Always-On Analog Compute-in-Memory Edge Accelerator. IEEE Micro 42(6): 76-87 (2022) - [c47]Kartikeya Bhardwaj, Dibakar Gope, James Ward, Paul N. Whatmough, Danny Loh:
Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge. DATE 2022: 418-423 - [c46]Zhi Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina:
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. HPCA 2022: 573-586 - [c45]Yiming Gan, Paul N. Whatmough, Jingwen Leng, Bo Yu, Shaoshan Liu, Yuhao Zhu:
Braum: Analyzing and Protecting Autonomous Machine Software Stack. ISSRE 2022: 85-96 - [c44]Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj, Paul N. Whatmough, Aleksandra Faust, Sabrina M. Neuman, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi:
Automatic Domain-Specific SoC Design for Autonomous Unmanned Aerial Vehicles. MICRO 2022: 300-317 - [c43]Igor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough:
UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units. NeurIPS 2022 - [i30]Igor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough:
UDC: Unified DNAS for Compressible TinyML Models. CoRR abs/2201.05842 (2022) - [i29]Kartikeya Bhardwaj, James Ward, Caleb Tung, Dibakar Gope, Lingchuan Meng, Igor Fedorov, Alex Chalfin, Paul N. Whatmough, Danny Loh:
Restructurable Activation Networks. CoRR abs/2208.08562 (2022) - [i28]Abhishek Tyagi, Yiming Gan, Shaoshan Liu, Bo Yu, Paul N. Whatmough, Yuhao Zhu:
Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators. CoRR abs/2212.02649 (2022) - 2021
- [c42]Jian Meng, Shreyas Kolala Venkataramanaiah, Chuteng Zhou, Patrick Hansen, Paul N. Whatmough, Jae-sun Seo:
FixyFPGA: Efficient FPGA Accelerator for Deep Neural Networks with High Element-Wise Sparsity and without External Memory Access. FPL 2021: 9-16 - [c41]Thierry Tambe, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei:
SM6: A 16nm System-on-Chip for Accurate and Noise-Robust Attention-Based NLP Applications : The 33rd Hot Chips Symposium - August 22-24, 2021. HCS 2021: 1-13 - [c40]Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama:
Federated Learning Based on Dynamic Regularization. ICLR 2021 - [c39]Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama:
Debiasing Model Updates for Improving Personalized Federated Training. ICML 2021: 21-31 - [c38]Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough:
Strong data processing inequality in neural networks with noisy neurons and its implications. ISIT 2021: 1170-1175 - [c37]Thierry Tambe, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei:
9.8 A 25mm2 SoC for IoT Devices with 18ms Noise-Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET. ISSCC 2021: 158-160 - [c36]Thierry Tambe, Coleman Hooper, Lillian Pentecost, Tianyu Jia, En-Yu Yang, Marco Donato, Victor Sanh, Paul N. Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei:
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference. MICRO 2021: 830-844 - [c35]Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough:
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. MLSys 2021 - [c34]Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu:
Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices. MLSys 2021 - [i27]Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough:
Information contraction in noisy binary neural networks and its implications. CoRR abs/2101.11750 (2021) - [i26]Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj, Paul N. Whatmough, Aleksandra Faust, Sabrina M. Neuman, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi:
Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots. CoRR abs/2102.02988 (2021) - [i25]Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu:
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices. CoRR abs/2102.07071 (2021) - [i24]Yu Feng, Patrick Hansen, Paul N. Whatmough, Guoyu Lu, Yuhao Zhu:
A LiDAR-Guided Framework for Video Enhancement. CoRR abs/2103.08764 (2021) - [i23]Zhi Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina:
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. CoRR abs/2107.07983 (2021) - [i22]Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama:
Federated Learning Based on Dynamic Regularization. CoRR abs/2111.04263 (2021) - [i21]Chuteng Zhou, Fernando García-Redondo, Julian Büchel, Irem Boybat, Xavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian, Manuel Le Gallo, Paul N. Whatmough:
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator. CoRR abs/2111.06503 (2021) - [i20]Kartikeya Bhardwaj, Dibakar Gope, James Ward, Paul N. Whatmough, Danny Loh:
Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge. CoRR abs/2112.14340 (2021) - 2020
- [j15]Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina:
Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference. IEEE Comput. Archit. Lett. 19(1): 34-37 (2020) - [j14]Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj, Paul N. Whatmough, Aleksandra Faust, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi:
The Sky Is Not the Limit: A Visual Performance Model for Cyber-Physical Co-Design in Autonomous Machines. IEEE Comput. Archit. Lett. 19(1): 38-42 (2020) - [j13]Paul N. Whatmough, Marco Donato, Glenn G. Ko, Sae Kyu Lee, David Brooks, Gu-Yeon Wei:
CHIPKIT: An Agile, Reusable Open-Source Framework for Rapid Test Chip Development. IEEE Micro 40(4): 32-40 (2020) - [j12]Sam Likun Xi, Yuan Yao, Kshitij Bhardwaj, Paul N. Whatmough, Gu-Yeon Wei, David Brooks:
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads. ACM Trans. Archit. Code Optim. 17(4): 39:1-39:26 (2020) - [c33]Glenn G. Ko, Yuji Chai, Marco Donato, Paul N. Whatmough, Thierry Tambe, Rob A. Rutenbar, Gu-Yeon Wei, David Brooks:
A Scalable Bayesian Inference Accelerator for Unsupervised Learning. Hot Chips Symposium 2020: 1-27 - [c32]Patrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu S. Talagala, David Hanwell, Matthew Mattina, Paul N. Whatmough:
ISP4ML: The Role of Image Signal Processing in Efficient Deep Learning Vision Systems. ICPR 2020: 2438-2445 - [c31]Igor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough:
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids. INTERSPEECH 2020: 4054-4058 - [c30]Ananda Samajdar, Jan Moritz Joseph, Yuhao Zhu, Paul N. Whatmough, Matthew Mattina, Tushar Krishna:
A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-Sim. ISPASS 2020: 58-68 - [c29]Yu Feng, Boyuan Tian, Tiancheng Xu, Paul N. Whatmough, Yuhao Zhu:
Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation. MICRO 2020: 1037-1050 - [c28]Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina:
Searching for Winograd-aware Quantized Networks. MLSys 2020 - [c27]Glenn G. Ko, Yuji Chai, Marco Donato, Paul N. Whatmough, Thierry Tambe, Rob A. Rutenbar, David Brooks, Gu-Yeon Wei:
A 3mm2 Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm. VLSI Circuits 2020: 1-2 - [i19]Paul N. Whatmough, Marco Donato, Glenn G. Ko, David Brooks, Gu-Yeon Wei:
CHIPKIT: An agile, reusable open-source framework for rapid test chip development. CoRR abs/2001.04504 (2020) - [i18]Chuteng Zhou, Prad Kadambi, Matthew Mattina, Paul N. Whatmough:
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation. CoRR abs/2001.04974 (2020) - [i17]Urmish Thakker, Paul N. Whatmough, Matthew Mattina, Jesse G. Beu:
Compressing Language Models using Doped Kronecker Products. CoRR abs/2001.08896 (2020) - [i16]Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina:
Searching for Winograd-aware Quantized Networks. CoRR abs/2002.10711 (2020) - [i15]Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina:
Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference. CoRR abs/2005.08098 (2020) - [i14]Igor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough:
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids. CoRR abs/2005.11138 (2020) - [i13]Yu Feng, Boyuan Tian, Tiancheng Xu, Paul N. Whatmough, Yuhao Zhu:
Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation. CoRR abs/2008.06967 (2020) - [i12]Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina:
Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration. CoRR abs/2009.02381 (2020) - [i11]Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough:
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. CoRR abs/2010.11267 (2020)
2010 – 2019
- 2019
- [j11]Jae-sun Seo, Yu Cao, Xin Li, Paul N. Whatmough:
Guest Editors' Introduction to the Special Section on Hardware and Algorithms for Energy-Constrained On-chip Machine Learning. ACM J. Emerg. Technol. Comput. Syst. 15(2): 14:1-14:2 (2019) - [j10]Jae-Sun Seo, Yu Cao, Xin Li, Paul N. Whatmough:
Guest Editors' Introduction: Hardware and Algorithms for Energy-Constrained On-Chip Machine Learning (Part 2). ACM J. Emerg. Technol. Comput. Syst. 15(4): 31:1-31:2 (2019) - [j9]Sae Kyu Lee, Paul N. Whatmough, David Brooks, Gu-Yeon Wei:
A 16-nm Always-On DNN Processor With Adaptive Clocking and Multi-Cycle Banked SRAMs. IEEE J. Solid State Circuits 54(7): 1982-1992 (2019) - [c26]Haitong Li, Mudit Bhargava, Paul N. Whatmough, H.-S. Philip Wong:
On-Chip Memory Technology Design Space Explorations for Mobile Deep Neural Network Accelerators. DAC 2019: 131 - [c25]Zacharias Hadjilambrou, Shidhartha Das, Paul N. Whatmough, David M. Bull, Yiannakis Sazeides:
GeST: An Automatic Framework For Generating CPU Stress-Tests. ISPASS 2019: 1-10 - [c24]Yu Feng, Paul N. Whatmough, Yuhao Zhu:
ASV: Accelerated Stereo Vision System. MICRO 2019: 643-656 - [c23]Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas K. Venkataramanaiah, Jae-sun Seo, Matthew Mattina:
FixyNN: Energy-Efficient Real-Time Mobile Computer Vision Hardware Acceleration via Transfer Learning. SysML 2019 - [c22]Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough:
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. NeurIPS 2019: 4978-4990 - [c21]Paul N. Whatmough, Sae Kyu Lee, Marco Donato, Hsea-Ching Hsueh, Sam Likun Xi, Udit Gupta, Lillian Pentecost, Glenn G. Ko, David M. Brooks, Gu-Yeon Wei:
A 16nm 25mm2 SoC with a 54.5x Flexibility-Efficiency Range from Dual-Core Arm Cortex-A53 to eFPGA and Cache-Coherent Accelerators. VLSI Circuits 2019: 34- - [i10]Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas K. Venkataramanaiah, Jae-sun Seo, Matthew Mattina:
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning. CoRR abs/1902.11128 (2019) - [i9]Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough:
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. CoRR abs/1905.12107 (2019) - [i8]Yu Feng, Paul N. Whatmough, Yuhao Zhu:
ASV: Accelerated Stereo Vision System. CoRR abs/1911.07919 (2019) - [i7]Patrick Hansen, Alexey Vilkin, Yury Khrustalev, James Imber, David Hanwell, Matthew Mattina, Paul N. Whatmough:
ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems. CoRR abs/1911.07954 (2019) - [i6]Sam Likun Xi, Yuan Yao, Kshitij Bhardwaj, Paul N. Whatmough, Gu-Yeon Wei, David Brooks:
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads. CoRR abs/1912.04481 (2019) - 2018
- [j8]Paul N. Whatmough, Sae Kyu Lee, David M. Brooks, Gu-Yeon Wei:
DNN Engine: A 28-nm Timing-Error Tolerant Sparse Deep Neural Network Processor for IoT Applications. IEEE J. Solid State Circuits 53(9): 2722-2731 (2018) - [c20]Brandon Reagen, Udit Gupta, Lillian Pentecost, Paul N. Whatmough, Sae Kyu Lee, Niamh Mulholland, David M. Brooks, Gu-Yeon Wei:
Ares: a framework for quantifying the resilience of deep neural networks. DAC 2018: 17:1-17:6 - [c19]Sae Kyu Lee, Paul N. Whatmough, Niamh Mulholland, Patrick Hansen, David Brooks, Gu-Yeon Wei:
A Wide Dynamic Range Sparse FC-DNN Processor with Multi-Cycle Banked SRAM Read and Adaptive Clocking in 16nm FinFET. ESSCIRC 2018: 158-161 - [c18]Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough:
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. ISCA 2018: 547-560 - [i5]Yuhao Zhu, Matthew Mattina, Paul N. Whatmough:
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective. CoRR abs/1801.06274 (2018) - [i4]Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough:
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. CoRR abs/1803.11232 (2018) - [i3]Ananda Samajdar, Yuhao Zhu, Paul N. Whatmough, Matthew Mattina, Tushar Krishna:
SCALE-Sim: Systolic CNN Accelerator. CoRR abs/1811.02883 (2018) - [i2]Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Matthew Mattina:
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning. CoRR abs/1812.01672 (2018) - 2017
- [b1]Brandon Reagen, Robert Adolf, Paul N. Whatmough, Gu-Yeon Wei, David M. Brooks:
Deep Learning for Computer Architects. Synthesis Lectures on Computer Architecture, Morgan & Claypool Publishers 2017, ISBN 978-3-031-00628-9 - [j7]Paul N. Whatmough, Shidhartha Das, Zacharias Hadjilambrou, David M. Bull:
Power Integrity Analysis of a 28 nm Dual-Core ARM Cortex-A57 Cluster Using an All-Digital Power Delivery Monitor. IEEE J. Solid State Circuits 52(6): 1643-1654 (2017) - [c17]Paul N. Whatmough, Sae Kyu Lee, Gu-Yeon Wei, David M. Brooks:
Sub-uJ deep neural networks for embedded applications. ACSSC 2017: 1912-1915 - [c16]Sreela Kodali, Patrick Hansen, Niamh Mulholland, Paul N. Whatmough, David M. Brooks, Gu-Yeon Wei:
Applications of Deep Neural Networks for Ultra Low Power IoT. ICCD 2017: 589-592 - [c15]Brandon Reagen, José Miguel Hernández-Lobato, Robert Adolf, Michael A. Gelbart, Paul N. Whatmough, Gu-Yeon Wei, David M. Brooks:
A case for efficient accelerator design space exploration via Bayesian optimization. ISLPED 2017: 1-6 - [c14]Paul N. Whatmough, Sae Kyu Lee, Hyunkwang Lee, Saketh Rama, David M. Brooks, Gu-Yeon Wei:
14.3 A 28nm SoC with a 1.2GHz 568nJ/prediction sparse deep-neural-network engine with >0.1 timing error rate tolerance for IoT applications. ISSCC 2017: 242-243 - 2016
- [j6]Jedrzej Kufel, Peter R. Wilson, Stephen Hill, Bashir M. Al-Hashimi, Paul N. Whatmough:
Sequence-Aware Watermark Design for Soft IP Embedded Processors. IEEE Trans. Very Large Scale Integr. Syst. 24(1): 276-289 (2016) - [c13]Brandon Reagen, Paul N. Whatmough, Robert Adolf, Saketh Rama, Hyunkwang Lee, Sae Kyu Lee, José Miguel Hernández-Lobato, Gu-Yeon Wei, David M. Brooks:
Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. ISCA 2016: 267-278 - [c12]Reza Ghanaatian, Paul N. Whatmough, Jeremy Constantin, Adam Teman, Andreas Burg:
A low-power correlator for wakeup receivers with algorithm pruning through early termination. ISCAS 2016: 2667-2670 - 2015
- [c11]Paul N. Whatmough, Shidhartha Das, David M. Bull:
Analysis of adaptive clocking technique for resonant supply voltage noise mitigation. ISLPED 2015: 128-133 - [c10]Shidhartha Das, Paul N. Whatmough, David M. Bull:
Modeling and characterization of the system-level Power Delivery Network for a dual-core ARM Cortex-A57 cluster in 28nm CMOS. ISLPED 2015: 146-151 - [c9]Paul N. Whatmough, Shidhartha Das, Zacharias Hadjilambrou, David M. Bull:
14.6 An all-digital power-delivery monitor for analysis of a 28nm dual-core ARM Cortex-A57 cluster. ISSCC 2015: 1-3 - [c8]Paul N. Whatmough, George Smart, Shidhartha Das, Yiannis Andreopoulos, David M. Bull:
A 0.6V all-digital body-coupled wakeup transceiver for IoT applications. VLSIC 2015: 98- - 2014
- [j5]Paul N. Whatmough, Shidhartha Das, David M. Bull:
A Low-Power 1-GHz Razor FIR Accelerator With Time-Borrow Tracking Pipeline and Approximate Error Correction in 65-nm CMOS. IEEE J. Solid State Circuits 49(1): 84-94 (2014) - [j4]Mohammad Ashraful Anam, Paul N. Whatmough, Yiannis Andreopoulos:
Precision-Energy-Throughput Scaling of Generic Matrix Multiplication and Convolution Kernels via Linear Projections. IEEE Trans. Circuits Syst. Video Technol. 24(11): 1860-1873 (2014) - [c7]Jedrzej Kufel, Peter R. Wilson, Stephen Hill, Bashir M. Al-Hashimi, Paul N. Whatmough, James Myers:
Clock-modulation based watermark for protection of embedded processors. DATE 2014: 1-6 - [i1]Mohammad Ashraful Anam, Paul N. Whatmough, Yiannis Andreopoulos:
Precision-Energy-Throughput Scaling Of Generic Matrix Multiplication and Convolution Kernels Via Linear Projections. CoRR abs/1411.2860 (2014) - 2013
- [j3]Paul N. Whatmough, Shidhartha Das, David M. Bull, Izzat Darwazeh:
Circuit-Level Timing Error Tolerance for Low-Power DSP Filters and Transforms. IEEE Trans. Very Large Scale Integr. Syst. 21(6): 989-999 (2013) - [c6]Mohammad Ashraful Anam, Paul N. Whatmough, Yiannis Andreopoulos:
Precision-energy-throughput scaling of generic matrix multiplication and discrete convolution kernels via linear projections. ESTIMedia 2013: 21-30 - [c5]Paul N. Whatmough, Shidhartha Das, David M. Bull:
A low-power 1GHz razor FIR accelerator with time-borrow tracking pipeline and approximate error correction in 65nm CMOS. ISSCC 2013: 428-429 - 2012
- [j2]Paul N. Whatmough, Marcus R. Perrett, Safa Isam, Izzat Darwazeh:
VLSI Architecture for a Reconfigurable Spectrally Efficient FDM Baseband Transmitter. IEEE Trans. Circuits Syst. I Regul. Pap. 59-I(5): 1107-1118 (2012) - [c4]Paul N. Whatmough, Shidhartha Das, David M. Bull, Izzat Darwazeh:
Selective time borrowing for DSP pipelines with hybrid voltage control loop. ASP-DAC 2012: 763-768 - 2011
- [c3]Paul N. Whatmough, Shidhartha Das, David M. Bull, Izzat Darwazeh:
Error-resilient low-power DSP via path-delay shaping. DAC 2011: 1008-1013 - [c2]Paul N. Whatmough, Marcus R. Perrett, Safa Isam, Izzat Darwazeh:
VLSI architecture for a reconfigurable Spectrally Efficient FDM baseband transmitter. ISCAS 2011: 1688-1691 - 2010
- [c1]Paul N. Whatmough, Izzat Darwazeh, David M. Bull, Shidhartha Das, Danny Kershaw:
A robust FIR filter with in situ error detection. ISCAS 2010: 4185-4188
2000 – 2009
- 2009
- [j1]Brian J. Minnis, Paul A. Moore, Paul N. Whatmough, Peter G. Blanken, Mark P. van der Heijden:
System-Efficiency Analysis of Power Amplifier Supply-Tracking Regimes in Mobile Transmitters. IEEE Trans. Circuits Syst. I Regul. Pap. 56-I(1): 268-279 (2009)
Coauthor Index
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Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-08-20 22:50 CEST by the dblp team
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