Title
Aggressive Fine-Grained Power Gating of NoC Buffers
Abstract
Power gating is effective for networks-on-chip (NoCs) to reduce the excessive leakage power dissipated by idle network components. Most existing NoC power-gating approaches rely on the routing algorithms to mitigate the power-gating blocking latency problem. When the network becomes faulty and fault-tolerant routing algorithms are applied, these approaches are no longer applicable or can seriously degrade the performance. Other approaches propose fine-grained buffer power gating, but they are too conservative in power saving due to the buffer backpressure flow control. To address these problems, we propose an aggressive fine-grained power gating of flit-sized buffer entries by adopting backpressureless flow control in an input-buffered network. The power-gating decisions are made based on the flit deflection rate. However, directly applying the backpressureless flow control leads to the difficulties of multiflit packet truncation and protocol deadlocks. Therefore, we modify the packet injection architecture to avoid packet truncation. This is done by chaining the local input port with a randomly chosen input port. Finally, we design a progressive recovery framework to handle both livelocks and protocol deadlocks. It does not need to truncate packets or strictly separate different message classes when the network is free of livelocks or protocol deadlocks. The experimental results show that with a hardware overhead of 9.6%, our design can save up to 59% network power consumption in both a fault-free and a faulty NoC with little zero-load latency penalty. Our design also approaches an ideal energy-proportional NoC because it can constantly reduce power consumption over a wide range of injection rates.
Year
DOI
Venue
2020
10.1109/TCAD.2020.3012170
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Keywords
DocType
Volume
Backpressureless,buffer,fine-grained,network-on-chip (NoC),power gating
Journal
39
Issue
ISSN
Citations 
11
0278-0070
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yibo Wu110.72
leibo liu2816116.95
Liang Wang31567158.46
Xiaohang Wang489553.93
Jie Han586366.92
Chenchen Deng6366.38
Shaojun Wei7555102.32