Author:
Stazi Giulia,Mastrandrea Antonio,Olivieri Mauro,Menichelli Francesco
Publisher
Springer International Publishing
Reference8 articles.
1. Liu, S., Pattabiraman, K., Moscibroda, T., Zorn, B.G.: Flikker: saving dram refresh-power through critical data partitioning. ACM SIGPLAN Not. 47(4), 213–224 (2012)
2. Lucas, J., Alvarez-Mesa, M., Andersch, M., Juurlink, B.: Sparkk: Quality-scalable approximate storage in dram. The memory forum, pp. 1–9 (2014)
3. Raha, A., Sutar, S., Jayakumar, H., Raghunathan, V.: Quality configurable approximate dram. IEEE Trans. Comput. 66(7), 1172–1187 (2017)
4. Frustaci, F., Blaauw, D., Sylvester, D., Alioto, M.: Approximate srams with dynamic energy-quality management. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 24(6), 2128–2141 (2016)
5. Nguyen, D.T., Kim, H., Lee, H.-J., Chang, I.-J.: An approximate memory architecture for a reduction of refresh power consumption in deep learning applications. In: IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp. 1–5 (2018)