TY - GEN
T1 - Millimeter wave wireless network on chip using deep reinforcement learning
AU - Jog, Suraj
AU - Liu, Zikun
AU - Franques, Antonio
AU - Fernando, Vimuth
AU - Hassanieh, Haitham
AU - Abadal, Sergi
AU - Torrellas, Josep
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/8/10
Y1 - 2020/8/10
N2 - Wireless Network-on-Chip (NoC) has emerged as a promising solution to scale chip multi-core processors to hundreds of cores. However, traditional medium access protocols fall short here since the traffic patterns on wireless NoCs tend to be very dynamic and can change drastically across different cores, different time intervals and different applications. In this work, we present NeuMAC, a unified approach that combines networking, architecture and AI to generate highly adaptive medium access protocols that can learn and optimize for the structure, correlations and statistics of the traffic patterns on the NoC. Our results show that NeuMAC can quickly adapt to NoC traffic to provide significant gains in terms of latency and overall execution time, improving the execution time by up to 1.69X - 3.74X.
AB - Wireless Network-on-Chip (NoC) has emerged as a promising solution to scale chip multi-core processors to hundreds of cores. However, traditional medium access protocols fall short here since the traffic patterns on wireless NoCs tend to be very dynamic and can change drastically across different cores, different time intervals and different applications. In this work, we present NeuMAC, a unified approach that combines networking, architecture and AI to generate highly adaptive medium access protocols that can learn and optimize for the structure, correlations and statistics of the traffic patterns on the NoC. Our results show that NeuMAC can quickly adapt to NoC traffic to provide significant gains in terms of latency and overall execution time, improving the execution time by up to 1.69X - 3.74X.
KW - Deep reinforcement learning
KW - Millimeter wave
KW - Wireless network-on-chip
UR - http://www.scopus.com/inward/record.url?scp=85115674956&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115674956&partnerID=8YFLogxK
U2 - 10.1145/3405837.3411396
DO - 10.1145/3405837.3411396
M3 - Conference contribution
AN - SCOPUS:85115674956
T3 - Proceedings of the SIGCOMM 2020 Poster and Demo Sessions, SIGCOMM 2020
SP - 70
EP - 72
BT - Proceedings of the SIGCOMM 2020 Poster and Demo Sessions, SIGCOMM 2020
PB - Association for Computing Machinery, Inc
T2 - 2020 Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication, SIGCOMM 2020
Y2 - 10 August 2020 through 14 August 2020
ER -