Maede Hemmat

According to our database, Maede Hemmat authored at least 10 papers between 2016 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

CAP'NN: A Class-aware Framework for Personalized Neural Network Inference.

Maedeh Hemmat, Joshua San Miguel, Azadeh Davoodi

ACM Trans. Embed. Comput. Syst., September, 2022

$\text{Edge}^{n}$ AI: Distributed Inference with Local Edge Devices and Minimal Latency.

Maedeh Hemmat, Azadeh Davoodi, Yu Hen Hu

Proceedings of the 27th Asia and South Pacific Design Automation Conference, 2022

2021

AirNN: A Featherweight Framework for Dynamic Input-Dependent Approximation of CNNs.

Maedeh Hemmat, Joshua San Miguel, Azadeh Davoodi

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2021

2020

CAP'NN: Class-Aware Personalized Neural Network Inference.

Maedeh Hemmat, Joshua San Miguel, Azadeh Davoodi

Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020

CRANIA: Unlocking Data and Value Reuse in Iterative Neural Network Architectures.

Maedeh Hemmat, Tejas Shah, Yuhua Chen, Joshua San Miguel

Proceedings of the 25th Asia and South Pacific Design Automation Conference, 2020

2019

Power-efficient ReRAM-aware CNN model generation.

Maedeh Hemmat, Azadeh Davoodi

Integr., 2019

Dynamic Reconfiguration of CNNs for Input-Dependent Approximation.

Maedeh Hemmat, Azadeh Davoodi

Proceedings of the 20th International Symposium on Quality Electronic Design, 2019

2017

Hybrid TFET-MOSFET circuit: A solution to design soft-error resilient ultra-low power digital circuit.

Maede Hemmat, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram

Integr., 2017

2016

Robust Hybrid TFET-MOSFET Circuits in Presence of Process Variations and Soft Errors.

Maedeh Hemmat, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram

Proceedings of the VLSI-SoC: System-on-Chip in the Nanoscale Era - Design, Verification and Reliability, 2016

Hybrid TFET-MOSFET circuits: An approach to design reliable ultra-low power circuits in the presence of process variation.

Maede Hemmat, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram

Proceedings of the 2016 IFIP/IEEE International Conference on Very Large Scale Integration, 2016