Low-power implementation of Mitchell's approximate logarithmic multiplication for convolutional neural networks. - Min Soo Kim 003; Alberto A. Del Barrio; Román Hermida; Nader Bagherzadeh (ASP-DAC, 2018)

A Cost-Efficient Iterative Truncated Logarithmic Multiplication for Convolutional Neural Networks. - Hyun Jin Kim 001; Min Soo Kim 003; Alberto A. Del Barrio; Nader Bagherzadeh (ARITH, 2019)

Design of Power-Efficient FPGA Convolutional Cores with Approximate Log Multiplier. - Leonardo Tavares Oliveira; Min Soo Kim 003; Alberto A. Del Barrio García; Nader Bagherzadeh; Ricardo Menotti (ESANN, 2019)

Efficient Mitchell's Approximate Log Multipliers for Convolutional Neural Networks. - Min Soo Kim 003; Alberto A. Del Barrio; Leonardo Tavares Oliveira; Román Hermida; Nader Bagherzadeh (IEEE Trans. Computers, 2019)

Cost-Efficient Approximate Log Multipliers for Convolutional Neural Networks. - Min Soo Kim 003 (2020)

PLAM: a Posit Logarithm-Approximate Multiplier for Power Efficient Posit-based DNNs. - Raul Murillo 001; Alberto A. Del Barrio; Guillermo Botella; Min Soo Kim 003; Hyunjin Kim 001; Nader Bagherzadeh (CoRR, 2021)

The Effects of Approximate Multiplication on Convolutional Neural Networks. - Min Soo Kim 003; Alberto A. Del Barrio; Hyun Jin Kim 001; Nader Bagherzadeh (IEEE Trans. Emerg. Top. Comput., 2022)

PLAM: A Posit Logarithm-Approximate Multiplier. - Raul Murillo 001; Alberto A. Del Barrio; Guillermo Botella; Min Soo Kim 003; Hyun Jin Kim 001; Nader Bagherzadeh (IEEE Trans. Emerg. Top. Comput., 2022)