| 2026 | IOLTS | RESIST : Structured Regularization from Weight Similarity to Weight Diversity for Improving Error Resilience of Neural Networks. | Maryam Eslami, Salim Ullah, Akash Kumar |
| 2025 | CASES | Special Sessions - Emerging Scope and Design Challenges for Approximate Computing: Optimizing Accuracy-PPA trade-offs and Beyond. | Siva Satyendra Sahoo, Bastien Deveautour, Marcello Traiola, Chongyan Gu, Yun Wu, Aditya Japa, Salim Ullah, Akash Kumar |
| 2025 | CASES | Design, Model, and Explore Approximate Arithmetic Operators with AI/ML: A Tutorial. | Salim Ullah, Siva Satyendra Sahoo, Akash Kumar |
| 2025 | FCCM | BiKA: Binarized KAN-inspired Neural Network for Efficient Hardware Accelerator Designs. | Yuhao Liu, Salim Ullah, Akash Kumar |
| 2025 | ICCAD | Invited Paper: Circuit and Architecture Design with Emerging Computing Paradigms. | Salim Ullah, Siva Satyendra Sahoo, Can Li, Chao Li, Liu Liu, Tomas Sousa Pereira, Bo Wen, Xunzhao Yin, Armin Darjani, Nima Kavand, Chakravarthy Bodla, Rupa Yashaswi Panduga, Aniruddh Holemadlu, Johannes Maly, Jonathan Frste, Samarth Vadia, Xiaobo Sharon Hu, Akash Kumar |
| 2024 | CASES | Enabling Energy-efficient AI Computing: Leveraging Application-specific Approximations : (Education Class). | Salim Ullah, Siva Satyendra Sahoo, Akash Kumar |
| 2024 | DSD | LeQC-At: Learning Quantization Configurations During Adversarial Training for Robust Deep Neural Networks. | Siddharth Gupta, Salim Ullah, Akash Kumar |
| 2024 | FCCM | BitSys: Bitwise Systolic Array Architecture for Multi-precision Quantized Hardware Accelerators. | Yuhao Liu, Salim Ullah, Akash Kumar |
| 2023 | ASPDAC | SyFAxO-GeN: Synthesizing FPGA-Based Approximate Operators with Generative Networks. | Rohit Ranjan, Salim Ullah, Siva Satyendra Sahoo, Akash Kumar |
| 2022 | ASPDAC | Multi-Precision Deep Neural Network Acceleration on FPGAs. | Negar Neda, Salim Ullah, Azam Ghanbari, Hoda Mahdiani, Mehdi Modarressi, Akash Kumar |
| 2022 | DSD | PosAx-O: Exploring Operator-level Approximations for Posit Arithmetic in Embedded AI/ML. | Amritha Immaneni, Salim Ullah, Suresh Nambi, Siva Satyendra Sahoo, Akash Kumar |
| 2022 | FPL | ERMES: Efficient Racetrack Memory Emulation System based on FPGA. | Fanny Spagnolo, Salim Ullah, Pasquale Corsonello, Akash Kumar |
| 2021 | DAC | CLAppED: A Design Framework for Implementing Cross-Layer Approximation in FPGA-based Embedded Systems. | Salim Ullah, Siva Satyendra Sahoo, Akash Kumar |
| 2020 | ASPDAC | LeAp: Leading-one Detection-based Softcore Approximate Multipliers with Tunable Accuracy. | Zahra Ebrahimi, Salim Ullah, Akash Kumar |
| 2020 | DATE | L2L: A Highly Accurate Log_2_Lead Quantization of Pre-trained Neural Networks. | Salim Ullah, Siddharth Gupta, Kapil Ahuja, Aruna Tiwari, Akash Kumar |
| 2019 | CISIS | A Comparative Analysis of Neural Networks and Enhancement of ELM for Short Term Load Forecasting. | Rahim Ullah, Nadeem Javaid, Ghulam Hafeez, Salim Ullah, Fahad Ahmad, Ashraf Ullah |
| 2018 | DAC | Untitled record | Salim Ullah, Sanjeev Sripadraj Murthy, Akash Kumar |
| 2018 | DAC | Area-optimized low-latency approximate multipliers for FPGA-based hardware accelerators. | Salim Ullah, Semeen Rehman, Bharath Srinivas Prabakaran, Florian Kriebel, Muhammad Abdullah Hanif, Muhammad Shafique, Akash Kumar |
| 2018 | DATE | DeMAS: An efficient design methodology for building approximate adders for FPGA-based systems. | Bharath Srinivas Prabakaran, Semeen Rehman, Muhammad Abdullah Hanif, Salim Ullah, Ghazal Mazaheri, Akash Kumar, Muhammad Shafique |