Salim Ullah

E-mail

Phone

Fax

Visitor's Address

salim.ullah@tu-dresden.de

+49 351 463-40819

+49 (0)351 463-39995

Helmholtzstrasse 18,  BAR-III62

Publications

  • 2021

  • 16. S. Ullah, S. S. Sahoo, A. Kumar, "CLAppED: A Design Framework for Implementing Cross-Layer Approximation in FPGA-based Embedded Systems" (to appear), In Proceeding: 2021 58th ACM/IEEE Design Automation Conference (DAC), pp. 1-6, Jul 2021. [Bibtex & Downloads]
  • 15. Suresh Nambi, Salim Ullah, Siva Satyendra Sahoo, Aditya Lohana, Farhad Merchant, Akash Kumar, "ExPAN(N)D: Exploring Posits for Efficient Artificial Neural Network Design in FPGA-based Systems", In IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), pp. 1–1, July 2021. [doi] [Bibtex & Downloads]
  • 14. Salim Ullah, Tuan Duy Anh Nguyen, Akash Kumar, "Energy-Efficient Low-Latency Signed Multiplier for FPGA-Based Hardware Accelerators", In IEEE Embedded Systems Letters, Institute of Electrical and Electronics Engineers (IEEE), vol. 13, no. 2, pp. 41–44, Jun 2021. [doi] [Bibtex & Downloads]
  • 13. Siva Satyendra Sahoo, Akhil Raj Baranwal, Salim Ullah, Akash Kumar, "MemOReL: A Memory-Oriented Optimization Approach to Reinforcement Learning on FPGA-Based Embedded Systems", Association for Computing Machinery, pp. 339–346, New York, NY, USA, June 2021. [doi] [Bibtex & Downloads]
  • 12. Salim Ullah, Semeen Rehman, Muhammad Shafique, Akash Kumar, "High-Performance Accurate and Approximate Multipliers for FPGA-based Hardware Accelerators", In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Institute of Electrical and Electronics Engineers (IEEE), pp. 1–1, 2021. [doi] [Bibtex & Downloads]
  • 2020

  • 11. Akhil Raj Baranwal, Salim Ullah, Siva Satyendra Sahoo, Akash Kumar, "ReLAccS: A Multi-level Approach to Accelerator Design for Reinforcement Learning on FPGA-based Systems", In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Institute of Electrical and Electronics Engineers (IEEE), pp. 1–1, 28 October 2020. [doi] [Bibtex & Downloads]
  • 10. Arlene John, Salim Ullah, Akash Kumar, Barry Cardiff, Deepu John, "An Approximate Binary Classifier for Data Integrity Assessment in IoT Sensors", In Proceeding: 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS), IEEE, Nov 2020. [doi] [Bibtex & Downloads]
  • 9. Zahra Ebrahimi Mamaghani, Salim Ullah, Akash Kumar, "SIMDive: Approximate SIMD Soft Multiplier-Divider for FPGAs with Tunable Accuracy", In Proceeding: 30th 2020 ACM Great Lakes Symposium on VLSI (GLSVLSI), September 2020. [Bibtex & Downloads]
  • 8. S. Gupta, S. Ullah, K. Ahuja, A. Tiwari, A. Kumar, "ALigN: A Highly Accurate Adaptive Layerwise Log_2_Lead Quantization of Pre-Trained Neural Networks", In IEEE Access, vol. 8, pp. 118899-118911, June 2020. [Bibtex & Downloads]
  • 7. S. Ullah, H. Schmidl, S. S. Sahoo, S. Rehman, A. Kumar, "Area-optimized Accurate and Approximate Softcore Signed Multiplier Architectures", In IEEE Transactions on Computers, April 2020. [Bibtex & Downloads]
  • 6. S. Ullah, S. Gupta, K. Ahuja, A. Tiwari, A. Kumar, "L2L: A HIGHLY ACCURATE LOG_2_LEAD QUANTIZATION OF PRE-TRAINED NEURAL NETWORKS", In Proceeding: 2020 Design, Automation Test in Europe Conference Exhibition (DATE), March 2020. [Bibtex & Downloads]
  • 5. Zahra Ebrahimi Mamaghani, Salim Ullah, Akash Kumar, "LeAp: Leading-one Detection-based Softcore Approximate Multipliers with Tunable Accuracy", In Proceeding: 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC), Jan 2020. [Bibtex & Downloads]
  • 2019

  • 4. Adarsha Balaji, Salim Ullah, Anup Das, Akash Kumar, "Design Methodology for Embedded Approximate Artificial Neural Networks", Proceedings of the 2019 on Great Lakes Symposium on VLSI, ACM, pp. 489–494, New York, NY, USA, May 2019. [doi] [Bibtex & Downloads]
  • 2018

  • 3. Salim Ullah, Sanjeev Sripadraj Murthy, Akash Kumar, "SMApproxlib: library of FPGA-based approximate multipliers", Proceedings of the 55th Annual Design Automation Conference, pp. 157, June 2018. [Bibtex & Downloads]
  • 2. Salim Ullah, Semeen Rehman, Bharath Srinivas Prabakaran, Florian Kriebel, Muhammad Abdullah Hanif, Muhammad Shafique, Akash Kumar, "Area-optimized low-latency approximate multipliers for FPGA-based hardware accelerators", Proceedings of the 55th Annual Design Automation Conference, pp. 159, June 2018. [Bibtex & Downloads]
  • 1. B. S. Prabakaran, S. Rehman, M. A. Hanif, S. Ullah, G. Mazaheri, A. Kumar, M. Shafique, "DeMAS: An efficient design methodology for building approximate adders for FPGA-based systems", In Proceeding: 2018 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 917-920, March 2018. [doi] [Bibtex & Downloads]