Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning. - Paul N. Whatmough; Chuteng Zhou; Patrick Hansen; Matthew Mattina (CoRR, 2018)

FixyNN: Energy-Efficient Real-Time Mobile Computer Vision Hardware Acceleration via Transfer Learning. - Paul N. Whatmough; Chuteng Zhou; Patrick Hansen; Shreyas K. Venkataramanaiah; Jae-sun Seo; Matthew Mattina (SysML, 2019)

FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning. - Paul N. Whatmough; Chuteng Zhou; Patrick Hansen; Shreyas K. Venkataramanaiah; Jae-sun Seo; Matthew Mattina (CoRR, 2019)

Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation. - Chuteng Zhou; Prad Kadambi; Matthew Mattina; Paul N. Whatmough (CoRR, 2020)

FixyFPGA: Efficient FPGA Accelerator for Deep Neural Networks with High Element-Wise Sparsity and without External Memory Access. - Jian Meng; Shreyas Kolala Venkataramanaiah; Chuteng Zhou; Patrick Hansen; Paul N. Whatmough; Jae-sun Seo (FPL, 2021)

Strong data processing inequality in neural networks with noisy neurons and its implications. - Chuteng Zhou; Quntao Zhuang; Matthew Mattina; Paul N. Whatmough (ISIT, 2021)

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. - Colby R. Banbury; Chuteng Zhou; Igor Fedorov; Ramon Matas Navarro; Urmish Thakker; Dibakar Gope; Vijay Janapa Reddi; Matthew Mattina; Paul N. Whatmough (MLSys, 2021)

Active multi-fidelity Bayesian online changepoint detection. - Gregory W. Gundersen; Diana Cai; Chuteng Zhou; Barbara E. Engelhardt; Ryan P. Adams (UAI, 2021)

Information contraction in noisy binary neural networks and its implications. - Chuteng Zhou; Quntao Zhuang; Matthew Mattina; Paul N. Whatmough (CoRR, 2021)

AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator. - 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 (CoRR, 2021)

UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units. - Igor Fedorov; Ramon Matas Navarro; Hokchhay Tann; Chuteng Zhou; Matthew Mattina; Paul N. Whatmough (NeurIPS, 2022)

UDC: Unified DNAS for Compressible TinyML Models. - Igor Fedorov; Ramon Matas Navarro; Hokchhay Tann; Chuteng Zhou; Matthew Mattina; Paul N. Whatmough (CoRR, 2022)

ML-HW Co-Design of Noise-Robust TinyML Models and Always-On Analog Compute-in-Memory Edge Accelerator. - 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 (IEEE Micro, 2022)

PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices. - Yuji Chai; Devashree Tripathy; Chuteng Zhou; Dibakar Gope; Igor Fedorov; Ramon Matas Navarro; David Brooks 001; Gu-Yeon Wei; Paul N. Whatmough (CoRR, 2023)

wav2pos: Sound Source Localization using Masked Autoencoders. - Axel Berg; Jens Gulin; Mark O'Connor; Chuteng Zhou; Karl Åström 002; Magnus Oskarsson (IPIN, 2024)

Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator. - Abhishek Tyagi; Reiley Jeyapaul; Chuteng Zhou; Paul N. Whatmough; Yuhao Zhu 001 (ISPASS, 2024)

Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point. - Bokun Wang; Axel Berg; Durmus Alp Emre Acar; Chuteng Zhou (CoRR, 2024)