TY - GEN
T1 - Modified Logarithmic Multiplication Approximation for Machine Learning
AU - Kouretas, Ioannis
AU - Paliouras, Vassilis
AU - Stouraitis, Thanos
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, a novel approximation that allows exploitation of the full potential of logarithmic multiplication is proposed. More specifically, the proposed approximation is quantified in terms of mean square error (MSE) and compared to a competitive recent publication. Subsequently, an LSTM network is used as an illustrative test case and the proposed approximation is validated in terms of the accuracy of the netowrk. It has been shown that for short data wordlengths, the proposed approximation can achieve small loss values, for the particular LSTM network. Finally, the circuit implementation of the logarithmic multiplier is synthesized in a 28 nm standard-cell library. Results show reduced hardware complexity for similar loss values on the specific LSTM network.
AB - In this paper, a novel approximation that allows exploitation of the full potential of logarithmic multiplication is proposed. More specifically, the proposed approximation is quantified in terms of mean square error (MSE) and compared to a competitive recent publication. Subsequently, an LSTM network is used as an illustrative test case and the proposed approximation is validated in terms of the accuracy of the netowrk. It has been shown that for short data wordlengths, the proposed approximation can achieve small loss values, for the particular LSTM network. Finally, the circuit implementation of the logarithmic multiplier is synthesized in a 28 nm standard-cell library. Results show reduced hardware complexity for similar loss values on the specific LSTM network.
UR - http://www.scopus.com/inward/record.url?scp=85166371526&partnerID=8YFLogxK
U2 - 10.1109/AICAS57966.2023.10168664
DO - 10.1109/AICAS57966.2023.10168664
M3 - Conference contribution
AN - SCOPUS:85166371526
T3 - AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding
BT - AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023
Y2 - 11 June 2023 through 13 June 2023
ER -