PLAM: A Posit Logarithm-Approximate Multiplier.
IEEE Trans. Emerg. Top. Comput., 2022
The Effects of Approximate Multiplication on Convolutional Neural Networks.
IEEE Trans. Emerg. Top. Comput., 2022
PLAM: a Posit Logarithm-Approximate Multiplier for Power Efficient Posit-based DNNs.
CoRR, 2021
Cost-Efficient Approximate Log Multipliers for Convolutional Neural Networks.
PhD thesis, 2020
Efficient Mitchell's Approximate Log Multipliers for Convolutional Neural Networks.
IEEE Trans. Computers, 2019
Design of Power-Efficient FPGA Convolutional Cores with Approximate Log Multiplier.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019
A Cost-Efficient Iterative Truncated Logarithmic Multiplication for Convolutional Neural Networks.
Proceedings of the 26th IEEE Symposium on Computer Arithmetic, 2019
Low-power implementation of Mitchell's approximate logarithmic multiplication for convolutional neural networks.
Proceedings of the 23rd Asia and South Pacific Design Automation Conference, 2018