SCALM: Towards Semantic Caching for Automated Chat Services with Large Language Models

L. A. Belady, “A study of replacement algorithms for a virtual-storage computer,” IBM Systems journal , vol. 5, no. 2, pp. 78–101, 1966

1966

Earlier work this paper cites.

T. Johnson and D. Shasha, “2q: A low overhead high performance buffer management replacement algorithm,” in the 20th International Conference on Very Large Data Bases (VLDB) , 1994

1994

Earlier work this paper cites.

D. Wessels, Web Caching . O’Reilly Media, Inc., 2001

2001

Earlier work this paper cites.

D. Lee, J. Choi, J.-H. Kim, S. H. Noh, S. L. Min, Y. Cho, and C. S. Kim, “Lrfu: A spectrum of policies that subsumes the least recently used and least frequently used policies,” IEEE Transactions on Computers , vol. 50, no. 12, pp. 1352–1361, 2001

2001

Earlier work this paper cites.

Y. Zhou, J. Philbin, and K. Li, “The multi-queue replacement algorithm for second level buffer caches,” in Proc. of the General Track: 2001 USENIX Annual Technical Conference (ATC) , 2001

2001

Earlier work this paper cites.

S. Jiang and X. Zhang, “Lirs: an efficient low inter-reference recency set replacement policy to improve buffer cache performance,” in Proc. of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems (SIGMETRICS) , 2002

2002

Earlier work this paper cites.

N. Megiddo and D. S. Modha, “Arc: a self-tuning, low overhead replacement cache,” in Proc. of the 2nd USENIX conference on File and storage technologies (FAST) , 2003

2003

Earlier work this paper cites.

A. Ahmad and L. Dey, “A k-mean clustering algorithm for mixed numeric and categorical data,” Data & Knowledge Engineering , vol. 63, no. 2, pp. 503–527, 2007

2007

Earlier work this paper cites.

F. Murtagh and P. Contreras, “Algorithms for hierarchical clustering: An overview,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , vol. 2, no. 1, pp. 86–97, 2012

2012

Earlier work this paper cites.

E. Schubert, J. Sander, M. Ester, H. P. Kriegel, and X. Xu, “Dbscan revisited, revisited: Why and how you should (still) use dbscan,” ACM Transactions on Database Systems (TODS) , vol. 42, no. 3, pp. 1–21, 2017

2017

Earlier work this paper cites.

M. Xu, M. Zhu, Y. Liu, F. X. Lin, and X. Liu, “Deepcache: Principled cache for mobile deep vision,” in Proc. of the 24th Annual International Conference on Mobile Computing and Networking (MobiCom) , 2018

2018

Earlier work this paper cites.

C. Xu, X. Ma, R. Shea, H. Wang, and J. Liu, “Enhancing performance and energy efficiency for hybrid workloads in virtualized cloud environment,” IEEE Transactions on Cloud Computing , vol. 9, no. 1, pp. 168–181, 2018

2018

Earlier work this paper cites.

Z. Song et al. , “Learning relaxed belady for content distribution network caching,” in Proc. of the 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI) , 2020

2020

Earlier work this paper cites.

E. Liu, M. Hashemi, K. Swersky, P. Ranganathan, and J. Ahn, “An imitation learning approach for cache replacement,” in Proc. of the 37th International Conference on Machine Learning (ICML) , 2020

2020

Cited alongside, same era.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proc. of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022

2022

Cited alongside, same era.

J. Ho, W. Chan, C. Saharia, J. Whang, R. Gao, A. Gritsenko, D. P. Kingma et al. , “Imagen video: High definition video generation with diffusion models,” arXiv preprint arXiv:2210.02303 , 2022

Original

2022

Cited alongside, same era.

C. Wu, J. Liang, L. Ji, F. Yang, Y. Fang, D. Jiang, and N. Duan, “Nüwa: Visual synthesis pre-training for neural visual world creation,” in Proc. of the 17th European Conference on Computer Vision (ECCV) , 2022

2022

Cited alongside, same era.

G. Xiao, J. Lin, M. Seznec, H. Wu, J. Demouth, and S. Han, “Smoothquant: Accurate and efficient post-training quantization for large language models,” in Proc. of the 40th International Conference on Machine Learning (ICML) , 2023

2023

Later among the works it cites.

G. Park et al. , “Lut-gemm: Quantized matrix multiplication based on luts for efficient inference in large-scale generative language models,” in Proc. of the 12th International Conference on Learning Representations (ICLR) , 2023

2023

Later among the works it cites.

L. Chen, M. Zaharia, and J. Zou, “Frugalgpt: How to use large language models while reducing cost and improving performance,” arXiv preprint arXiv:2305.05176 , 2023

Original

2023

Later among the works it cites.

S. Kim, K. Mangalam, J. Malik, M. W. Mahoney, A. Gholami, and K. Keutzer, “Big little transformer decoder,” arXiv preprint arXiv:2302.07863 , 2023

Original

2023

Later among the works it cites.