WISCAD -- Wisconsin Computer-Aided Design Group University of Wisconsin - Madison

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CAP’NN: A Class-aware Framework for Personalized Neural Network Inference
CAP’NN: A Class-aware Framework for Personalized Neural Network Inference
Maedeh Hemmat, Joshua San Miguel, Azadeh Davoodi
ACM Transactions on Embedded Computing Systems  ·  30 Sep 2022  ·  doi:10.1145/3520126
$\text{Edge}^{n}$ AI: Distributed Inference with Local Edge Devices and Minimal Latency
Edgen AI: Distributed Inference with Local Edge Devices and Minimal Latency
Maedeh Hemmat, Azadeh Davoodi, Yu Hen Hu
2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)  ·  17 Jan 2022  ·  doi:10.1109/ASP-DAC52403.2022.9712496

2021

AirNN: A Featherweight Framework for Dynamic Input-Dependent Approximation of CNNs
AirNN: A Featherweight Framework for Dynamic Input-Dependent Approximation of CNNs
Maedeh Hemmat, Joshua San Miguel, Azadeh Davoodi
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems  ·  01 Oct 2021  ·  doi:10.1109/TCAD.2020.3033750

2020

CAP’NN: Class-Aware Personalized Neural Network Inference
CAP’NN: Class-Aware Personalized Neural Network Inference
Maedeh Hemmat, Joshua San Miguel, Azadeh Davoodi
2020 57th ACM/IEEE Design Automation Conference (DAC)  ·  01 Jul 2020  ·  doi:10.1109/DAC18072.2020.9218741

2019

Power-efficient ReRAM-aware CNN model generation
Power-efficient ReRAM-aware CNN model generation
Maedeh Hemmat, Azadeh Davoodi
Integration  ·  01 Nov 2019  ·  doi:10.1016/J.VLSI.2019.08.003
Dynamic Reconfiguration of CNNs for Input-Dependent Approximation
Dynamic Reconfiguration of CNNs for Input-Dependent Approximation
Maedeh Hemmat, Azadeh Davoodi
20th International Symposium on Quality Electronic Design (ISQED)  ·  01 Mar 2019  ·  doi:10.1109/ISQED.2019.8697843

2018

Power-Efficient ReRAM-Aware CNN Model Generation
Power-Efficient ReRAM-Aware CNN Model Generation
Maedeh Hemmat, Azadeh Davoodi
2018 IEEE 36th International Conference on Computer Design (ICCD)  ·  01 Oct 2018  ·  doi:10.1109/ICCD.2018.00033

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