TY - GEN
T1 - SCNoCs
T2 - 29th Asia and South Pacific Design Automation Conference, ASP-DAC 2024
AU - Jiang, Fan
AU - Li, Chengeng
AU - Chen, Lin
AU - Liu, Jiaqi
AU - Zhang, Wei
AU - Xu, Jiang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In-network compression has been proposed recently to support efficient communication. However, we find employing compression blindly cannot always pay off since de/compression leads to extra packet transmission delay. We thereby propose selective compression which compresses data adaptively based on network state and predicted compression ratio. Moreover, we observe that simply applying selective compression in a conventional single network is not energy efficient. Therefore, we propose SCNoCs, a heterogeneous Multi-NoC (Main-Net and HelperNet) architecture with the support of selective compression and power gating. SCNoCs can dynamically adjust the policy of selective compression and the utilization degree of the Helper-Net according to the network state at runtime. Experimental results show that our selective compression outperforms conventional compression by 1.5 × . Besides, our proposed SCNoCs achieves comparable performance while reducing energy consumption by 43.4%, compared with the baseline.
AB - In-network compression has been proposed recently to support efficient communication. However, we find employing compression blindly cannot always pay off since de/compression leads to extra packet transmission delay. We thereby propose selective compression which compresses data adaptively based on network state and predicted compression ratio. Moreover, we observe that simply applying selective compression in a conventional single network is not energy efficient. Therefore, we propose SCNoCs, a heterogeneous Multi-NoC (Main-Net and HelperNet) architecture with the support of selective compression and power gating. SCNoCs can dynamically adjust the policy of selective compression and the utilization degree of the Helper-Net according to the network state at runtime. Experimental results show that our selective compression outperforms conventional compression by 1.5 × . Besides, our proposed SCNoCs achieves comparable performance while reducing energy consumption by 43.4%, compared with the baseline.
UR - http://www.scopus.com/inward/record.url?scp=85189304367&partnerID=8YFLogxK
U2 - 10.1109/ASP-DAC58780.2024.10473870
DO - 10.1109/ASP-DAC58780.2024.10473870
M3 - Conference Proceeding
AN - SCOPUS:85189304367
T3 - Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
SP - 13
EP - 18
BT - ASP-DAC 2024 - 29th Asia and South Pacific Design Automation Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 January 2024 through 25 January 2024
ER -