TY - GEN
T1 - Data analysis & prediction for NAND flash decoding status
AU - Liao, Yen Chin
AU - Huang, Ching Hui
AU - Zeng, Cloud
AU - Chang, Hsie-Chia
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/5
Y1 - 2017/6/5
N2 - This paper investigates the feasibility of predicting the NAND flash decoding status by machine learning algorithms. The memory system can handle the future decoding failure in advance according to the prediction results so that to relieve the penalties. Several data preprocessing techniques to improve the accuracy are addressed. A thorough analysis flow is given and the experimental results show significant improvements. Incorporating with proper memory error handling schemes, a 34% improvement in throughput can be achieved.
AB - This paper investigates the feasibility of predicting the NAND flash decoding status by machine learning algorithms. The memory system can handle the future decoding failure in advance according to the prediction results so that to relieve the penalties. Several data preprocessing techniques to improve the accuracy are addressed. A thorough analysis flow is given and the experimental results show significant improvements. Incorporating with proper memory error handling schemes, a 34% improvement in throughput can be achieved.
UR - http://www.scopus.com/inward/record.url?scp=85023623373&partnerID=8YFLogxK
U2 - 10.1109/IMW.2017.7939076
DO - 10.1109/IMW.2017.7939076
M3 - Conference contribution
AN - SCOPUS:85023623373
T3 - 2017 IEEE 9th International Memory Workshop, IMW 2017
BT - 2017 IEEE 9th International Memory Workshop, IMW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Memory Workshop, IMW 2017
Y2 - 14 May 2017 through 17 May 2017
ER -