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Mehdi Ghasemirahaghi
Mehdi Ghasemirahaghi, Assistant Professor
Ph.D. Arizona State University, 2024
Mehdi’s research interests include Internet of Things (IoT), energy-aware computing, and computation offloading at the edge. His focus is on enabling the energy-efficient execution of machine learning workloads on resource-constrained commercial-off-the-shelf IoT devices. His research can be applied to a variety of use cases including but not limited to agriculture, robotics, AR/VR, and healthcare. He has worked on several projects ranging from circuit-level to system-level optimization of embedded systems.
He has served as a reviewer for several conferences and journals including Design Automation Conference (DAC), IEEE Transactions on Computer (TC), IEEE Transactions on Sustainable Computing (TSUSC), ACM Transactions on Embedded Computing Systems (TECS), and IEEE Embedded Systems Letter (ESL).
Engineering E-120
mehdi.g@siu.edu
Selected Publications
M. Farhadi, M. Ghasemi and Y. Yang, “A Novel Design of Adaptive and Hierarchical Convolutional Neural Networks using Partial Reconfiguration on FPGA”, 2019 IEEE High Performance Extreme Computing Conference (HPEC), Boston, USA, 2019
M. Farhadi, M. Ghasemi, S.Vrudhula, and Y. Yang, “Enabling Incremental Knowledge Transfer for Object Detection at the Edge”, LPCVC CVPR, USA, 2020
M. Ghasemi, S. Heidari, Y. Kim, A. Lamb, C. Wu, and S.Vrudhula, “Energy-Efficient Mapping for a Network of DNN Models at the Edge”, SMARTCOMP 2021
M. Ghasemi, D. Rakhmatov, C. Wu, and S.Vrudhula, “EdgeWise: Energy-efficient CNN Computation on Edge Devices under Stochastic Communication Delays”, ACM Transactions on Embedded Computing Systems, Vol. 21, No. 5, 2022
S. Heidari, M. Ghasemi, Y. Kim, A. Lamb, C. Wu, and S.Vrudhula, “CAMDNN: Content-Aware Mapping of a Network of Deep Neural Networks on Edge MPSoCs”, IEEE Transactions on Computers, Vol. 71, No. 12, 2022