Fetching the paper…
Reading the bibliography…
Deploying Large Language Models (LLMs) on edge devices remains challenging due to their quadratically increasing computations with the sequence length.
1905
Earlier work this paper cites.
1905
Earlier work this paper cites.
1911
Earlier work this paper cites.
D. Neil, J. H. Lee, T. Delbruck, and S.-C. Liu, “Delta networks for optimized recurrent network computation,” in International conference on machine learning . PMLR, 2017, pp. 2584–2593
2017
Earlier work this paper cites.
C. Gao, D. Neil, E. Ceolini, S.-C. Liu, and T. Delbruck, “Deltarnn: A power-efficient recurrent neural network accelerator,” in Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays , 2018, pp. 21–30
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
T. Mihaylov, P. Clark, T. Khot, and A. Sabharwal, “Can a suit of armor conduct electricity? a new dataset for open book question answering,” in EMNLP , 2018
2018
Earlier work this paper cites.
P. Rajpurkar, J. Zhang, and P. Liang, “Know what you don’t know: Unanswerable questions for squad,” in ACL 2018 , 2018
2018
Earlier work this paper cites.
“Winogrande: An adversarial winograd schema challenge at scale,” 2019
2019
Earlier work this paper cites.
C. Gao, A. Rios-Navarro, X. Chen, S.-C. Liu, and T. Delbruck, “Edgedrnn: Recurrent neural network accelerator for edge inference,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems , vol. 10, no. 4, pp. 419–432, 2020
2020
Earlier work this paper cites.
A. Habibian, D. Abati, T. S. Cohen, and B. E. Bejnordi, “Skip-convolutions for efficient video processing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2695–2704
2021
Earlier work this paper cites.
C. Gao, T. Delbruck, and S.-C. Liu, “Spartus: A 9.4 top/s fpga-based lstm accelerator exploiting spatio-temporal sparsity,” IEEE Transactions on Neural Networks and Learning Systems , vol. 35, no. 1, pp. 1098–1112, 2022
2022
Cited alongside, same era.
M. Parger, C. Tang, C. D. Twigg, C. Keskin, R. Wang, and M. Steinberger, “Deltacnn: End-to-end cnn inference of sparse frame differences in videos,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 12 487–12 496
2022
Cited alongside, same era.
2022
Cited alongside, same era.
S. Bubeck, V. Chadrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al. , “Sparks of artificial general intelligence: Early experiments with gpt-4,” 2023
2023
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
M. Parger, C. Tang, T. Neff, C. D. Twigg, C. Keskin, R. Wang, and M. Steinberger, “Motiondeltacnn: Sparse cnn inference of frame differences in moving camera videos with spherical buffers and padded convolutions,” in ICCV , 2023, pp. 17 246–17 255. [Online]. Available: https://doi.org/10.1109/ICCV51070.2023.01586
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Cited alongside, same era.
J. Lin, J. Tang, H. Tang, S. Yang, W.-M. Chen, W.-C. Wang, G. Xiao, X. Dang, C. Gan, and S. Han, “Awq: Activation-aware weight quantization for on-device llm compression and acceleration,” Proceedings of Machine Learning and Systems , vol. 6, pp. 87–100, 2024
2024
Cited alongside, same era.
Y. Li, Y. Huang, B. Yang, B. Venkitesh, A. Locatelli, H. Ye, T. Cai, P. Lewis, and D. Chen, “Snapkv: Llm knows what you are looking for before generation,” Advances in Neural Information Processing Systems , vol. 37, pp. 22 947–22 970, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
L. Gao, J. Tow, B. Abbasi, S. Biderman, S. Black, A. DiPofi, C. Foster, L. Golding, J. Hsu, A. Le Noac’h, H. Li, K. McDonell, N. Muennighoff, C. Ociepa, J. Phang, L. Reynolds, H. Schoelkopf, A. Skowron, L. Sutawika, E. Tang, A. Thite, B. Wang, K. Wang, and A. Zou, “The language model evaluation harness,” 07 2024. [Online]. Available: https://zenodo.org/records/12608602
2024
Later among the works it cites.
2025
Closest in time.
2025
Closest in time.
Q. Chen, K. Kim, C. Gao, S. Zhou, T. Jang, T. Delbruck, and S.-C. Liu, “Deltakws: A 65nm 36nj/decision bio-inspired temporal-sparsity-aware digital keyword spotting ic with 0.6v near-threshold sram,” IEEE Transactions on Circuits and Systems for Artificial Intelligence , vol. 2, no. 1, pp. 79–87, 2025
2025
Closest in time.