PIR-DSP: An FPGA DSP Block Architecture for Multi-precision Deep Neural Networks. - Seyedramin Rasoulinezhad; Hao Zhou 008; Lingli Wang; Philip H. W. Leong (FCCM, 2019)

MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency. - Seyedramin Rasoulinezhad; Sean Fox; Hao Zhou 008; Lingli Wang; David Boland; Philip H. W. Leong (FPT, 2019)

LUXOR: An FPGA Logic Cell Architecture for Efficient Compressor Tree Implementations. - Seyedramin Rasoulinezhad; Siddhartha 003; Hao Zhou 008; Lingli Wang; David Boland; Philip H. W. Leong (FPGA, 2020)

MLBlocks: FPGA Blocks for Machine Learning Applications. - Seyedramin Rasoulinezhad; David Boland; Philip H. W. Leong (FPGA, 2021)

APIR-DSP: An approximate PIR-DSP architecture for error-tolerant applications. - Yuan Dai; Simin Liu; Yao Lu; Hao Zhou 008; Seyedramin Rasoulinezhad; Philip H. W. Leong; Lingli Wang (FPT, 2021)

A Block Minifloat Representation for Training Deep Neural Networks. - Sean Fox; Seyedramin Rasoulinezhad; Julian Faraone; David Boland; Philip H. W. Leong (ICLR, 2021)

Modified Joint Channel-and-Data Estimation for One-Bit Massive MIMO. - Mohammadhossein Bahari; Seyedramin Rasoulinezhad; Mahdi Amiri; Farzam Gilani; Saeed Saadatnejad; Seyed Alireza Nezamalhosseini; Mahdi Shabany (ISCAS, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (CoRR, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (Frontiers Big Data, 2022)

NITI: Training Integer Neural Networks Using Integer-Only Arithmetic. - Maolin Wang 002; Seyedramin Rasoulinezhad; Philip H. W. Leong; Hayden Kwok-Hay So (IEEE Trans. Parallel Distributed Syst., 2022)

Rethinking Embedded Blocks for Machine Learning Applications. - Seyedramin Rasoulinezhad; Esther Roorda; Steve Wilton; Philip H. W. Leong; David Boland (ACM Trans. Reconfigurable Technol. Syst., 2022)

FPGA Architecture Exploration for DNN Acceleration. - Esther Roorda; Seyedramin Rasoulinezhad; Philip H. W. Leong; Steven J. E. Wilton (ACM Trans. Reconfigurable Technol. Syst., 2022)

Corrigendum: Applications and techniques for fast machine learning in science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci-Memik; et al. (Frontiers Big Data, 2024)