A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding. - Daniel Gerlinghoff; Zhehui Wang; Xiaozhe Gu; Rick Siow Mong Goh; Tao Luo 014 (DATE, 2022)

E3NE: An End-to-End Framework for Accelerating Spiking Neural Networks With Emerging Neural Encoding on FPGAs. - Daniel Gerlinghoff; Zhehui Wang; Xiaozhe Gu; Rick Siow Mong Goh; Tao Luo 014 (IEEE Trans. Parallel Distributed Syst., 2022)

DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs. - Myat Thu Linn Aung; Daniel Gerlinghoff; Chuping Qu; Liwei Yang; Tian Huang; Rick Siow Mong Goh; Tao Luo 014; Weng-Fai Wong (IEEE Trans. Computers, 2023)

Desire backpropagation: A lightweight training algorithm for multi-layer spiking neural networks based on spike-timing-dependent plasticity. - Daniel Gerlinghoff; Tao Luo 014; Rick Siow Mong Goh; Weng-Fai Wong (Neurocomputing, 2023)

Table-Lookup MAC: Scalable Processing of Quantised Neural Networks in FPGA Soft Logic. - Daniel Gerlinghoff; Benjamin Chen Ming Choong; Rick Siow Mong Goh; Weng-Fai Wong; Tao Luo 014 (FPGA, 2024)

Is quantum optimization ready? An effort towards neural network compression using adiabatic quantum computing. - Zhehui Wang; Benjamin Chen Ming Choong; Tian Huang; Daniel Gerlinghoff; Rick Siow Mong Goh; Cheng Liu 008; Tao Luo 014 (Future Gener. Comput. Syst., 2026)