This is a repository for logarithmic Functional Units.
This code, or a variation of it, has been employed in the following articles. If you are interested in using this code, it would be fair if you just cite us.
Here is a list of papers based on this code:
M. S. Kim, A. A. Del Barrio Garcia, H. Kim and N. Bagherzadeh, "The Effects of Approximate Multiplication on Convolutional Neural Networks," in IEEE Transactions on Emerging Topics in Computing, doi: 10.1109/TETC.2021.3050989.
H. Kim, M. S. Kim, A. A. Del Barrio and N. Bagherzadeh, "A Cost-Efficient Iterative Truncated Logarithmic Multiplication for Convolutional Neural Networks," 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), 2019, pp. 108-111, doi: 10.1109/ARITH.2019.00029.
L.T. Oliveira, M.S. Kim, A. A. Del Barrio Garcia, N. Bagherzadeh and R. Menotti, "Design of Power-Efficient FPGA Convolutional Cores with Approximate Log Multiplier", 2019 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
M. S. Kim, A. A. Del Barrio Garcia, L. T. Oliveira, R. Hermida and N. Bagherzadeh, "Efficient Mitchell's Approximate Log Multipliers for Convolutional Neural Networks," in IEEE Transactions on Computers. doi: 10.1109/TC.2018.2880742
M. S. Kim, A. A. Del Barrio, R. Hermida and N. Bagherzadeh, "Low-power implementation of Mitchell's approximate logarithmic multiplication for convolutional neural networks," 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC), Jeju, 2018, pp. 617-622. doi: 10.1109/ASPDAC.2018.8297391
This work has been supported by the EU (FEDER) and the Spanish MINECO and CM under grants S2018/TCS-4423 and RTI2018-093684-B-I00.