Seyedramin Rasoulinezhad

Orcid: 0000-0002-7366-8533

According to our database1, Seyedramin Rasoulinezhad authored at least 14 papers between 2019 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2022
FPGA Architecture Exploration for DNN Acceleration.
ACM Trans. Reconfigurable Technol. Syst., 2022

Rethinking Embedded Blocks for Machine Learning Applications.
ACM Trans. Reconfigurable Technol. Syst., 2022

NITI: Training Integer Neural Networks Using Integer-Only Arithmetic.
IEEE Trans. Parallel Distributed Syst., 2022

Applications and Techniques for Fast Machine Learning in Science.
Frontiers Big Data, 2022

2021
Applications and Techniques for Fast Machine Learning in Science.
CoRR, 2021

Modified Joint Channel-and-Data Estimation for One-Bit Massive MIMO.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2021

A Block Minifloat Representation for Training Deep Neural Networks.
Proceedings of the 9th International Conference on Learning Representations, 2021

APIR-DSP: An approximate PIR-DSP architecture for error-tolerant applications.
Proceedings of the International Conference on Field-Programmable Technology, 2021

MLBlocks: FPGA Blocks for Machine Learning Applications.
Proceedings of the FPGA '21: The 2021 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, Virtual Event, USA, February 28, 2021

2020
LUXOR: An FPGA Logic Cell Architecture for Efficient Compressor Tree Implementations.
CoRR, 2020

MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency.
CoRR, 2020

LUXOR: An FPGA Logic Cell Architecture for Efficient Compressor Tree Implementations.
Proceedings of the FPGA '20: The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2020

2019
MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency.
Proceedings of the International Conference on Field-Programmable Technology, 2019

PIR-DSP: An FPGA DSP Block Architecture for Multi-precision Deep Neural Networks.
Proceedings of the 27th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2019


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