no code implementations • 26 Jan 2023 • Yuji Chai, Devashree Tripathy, Chuteng Zhou, Dibakar Gope, Igor Fedorov, Ramon Matas, David Brooks, Gu-Yeon Wei, Paul Whatmough
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as latency, power, and memory footprint, for an arbitrary DNN on a target hardware platform is essential to the design of DNN based models.
no code implementations • 15 Jan 2022 • Igor Fedorov, Ramon Matas, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul Whatmough
Deploying TinyML models on low-cost IoT hardware is very challenging, due to limited device memory capacity.
no code implementations • 10 Nov 2021 • Chuteng Zhou, Fernando Garcia Redondo, Julian Büchel, Irem Boybat, Xavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian, Manuel Le Gallo, Paul N. Whatmough
We also describe AON-CiM, a programmable, minimal-area phase-change memory (PCM) analog CiM accelerator, with a novel layer-serial approach to remove the cost of complex interconnects associated with a fully-pipelined design.
1 code implementation • 26 Mar 2021 • Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams
We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints.
no code implementations • 28 Jan 2021 • Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough
Our SDPI can be applied to various information processing systems, including neural networks and cellular automata.
1 code implementation • 21 Oct 2020 • Colby Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough
To address this challenge, neural architecture search (NAS) promises to help design accurate ML models that meet the tight MCU memory, latency and energy constraints.
Ranked #1 on Keyword Spotting on Google Speech Commands V2 12
no code implementations • 14 Jan 2020 • Chuteng Zhou, Prad Kadambi, Matthew Mattina, Paul N. Whatmough
Hence, for successful deployment on analog accelerators, it is essential to be able to train deep neural networks to be robust to random continuous noise in the network weights, which is a somewhat new challenge in machine learning.
1 code implementation • 27 Feb 2019 • Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas Kolala Venkataramanaiah, Jae-sun Seo, Matthew Mattina
Over a suite of six datasets we trained models via transfer learning with an accuracy loss of $<1\%$ resulting in up to 11. 2 TOPS/W - nearly $2 \times$ more efficient than a conventional programmable CNN accelerator of the same area.
no code implementations • 4 Dec 2018 • Paul Whatmough, Chuteng Zhou, Patrick Hansen, Matthew Mattina
On-device CNN inference for real-time computer vision applications can result in computational demands that far exceed the energy budgets of mobile devices.