no code implementations • 9 May 2023 • Myat Thu Linn Aung, Daniel Gerlinghoff, Chuping Qu, Liwei Yang, Tian Huang, Rick Siow Mong Goh, Tao Luo, Weng-Fai Wong
Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency.
1 code implementation • 10 Nov 2022 • Daniel Gerlinghoff, Tao Luo, Rick Siow Mong Goh, Weng-Fai Wong
Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance.
no code implementations • 6 Jun 2022 • Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu, Rick Siow Mong Goh, Tao Luo
However, current accelerators for SNN cannot well support the emerging encoding schemes.
1 code implementation • 19 Nov 2021 • Daniel Gerlinghoff, Zhehui Wang, Xiaozhe Gu, Rick Siow Mong Goh, Tao Luo
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators.
no code implementations • 29 Sep 2021 • Tao Luo, Zhehui Wang, Daniel Gerlinghoff, Rick Siow Mong Goh, Weng-Fai Wong
In this paper, we propose BLUnet, a table lookup-based DNN model with bit-serialized input to overcome this challenge.