Salar Shakibhamedan

According to our database, Salar Shakibhamedan authored at least 8 papers between 2022 and 2025.

Collaborative distances:

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

2025

FairTabGen: Unifying Counterfactual and Causal Fairness in Synthetic Tabular Data Generation.

Nitish Nagesh, Salar Shakibhamedan, Mahdi Bagheri, Ziyu Wang, Nima TaheriNejad, Axel Jantsch, Amir M. Rahmani

CoRR, August, 2025

2024

ACE-CNN: Approximate Carry Disregard Multipliers for Energy-Efficient CNN-Based Image Classification.

Salar Shakibhamedan, Nima Amirafshar, Ahmad Sedigh Baroughi, Hadi Shahriar Shahhoseini, Nima TaheriNejad

IEEE Trans. Circuits Syst. I Regul. Pap., May, 2024

Adaptive approximate computing in edge AI and IoT applications: A review.

Hans Jakob Damsgaard, Antoine Grenier, Dewant Katare, Zain Taufique, Salar Shakibhamedan, Tiago Troccoli, Georgios Chatzitsompanis, Anil Kanduri, Aleksandr Ometov, Aaron Yi Ding, Nima Taherinejad, Georgios Karakonstantis, Roger F. Woods, Jari Nurmi

J. Syst. Archit., 2024

IMPLY-based Approximate Full Adders for Efficient Arithmetic Operations in Image Processing and Machine Learning.

Melanie Qiu, Caoyueshan Fan, Gulafshan, Salar Shakibhamedan, Fabian Seiler, Nima TaheriNejad

CoRR, 2024

Harnessing Approximate Computing for Machine Learning.

Salar Shakibhamedan, Amin Aminifar, Luke Vassallo, Nima TaheriNejad

Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2024

RecogNoise: Machine-Learning-Based Recognition of Noisy Segments in Electrocardiogram Signals.

Amin Aminifar, Soheil Khooyooz, Anice Jahanjoo, Salar Shakibhamedan, Nima TaheriNejad

Proceedings of the IEEE International Symposium on Circuits and Systems, 2024

OPTIMA: Design-Space Exploration of Discharge-Based In-SRAM Computing: Quantifying Energy-Accuracy Trade-offs.

Saeed Seyedfaraji, Severin Jager, Salar Shakibhamedan, Asad Aftab, Semeen Rehman

Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

2022

Energy-aware Adaptive Approximate Computing for Deep Learning Applications.

Nima TaheriNejad, Salar Shakibhamedan

Proceedings of the IEEE Computer Society Annual Symposium on VLSI, 2022