Dynamic Traffic Distribution
among Hierarchy Levels in
Hierarchical Networks-on-Chip
Ran Manevich, Israel Cidon, and Avinoam Kolodny
Electrical Engineering Department
Technion – Israel Institute of Technology
Haifa, Israel
NOCS 2013
QNoC
Research
Group
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Hierarchical un-clustered NoCs
Hierarchical Rings
S. Bourduas and, Z. Zilic, “Latency reduction of global traffic
in wormhole-routed meshes using hierarchical rings for global
routing.” ASAP 2007.
PyraMesh
R. Manevich, I Cidon and, A. Kolodny. “Handling global traffic in
future CMP NoCs” SLIP 2012.
Routing in hierarchical NoCs
Phase 1
Ascent to the highest
level (LMAX).
Phase 2
Travel on LMAX towards
the destination.
Phase 3
Descent from LMAX and
reach the destination.
Traffic distribution among
hierarchy levels
Highest level LMAX
defines distribution of
traffic among
hierarchy levels.
LMAX =
Packets distribution policy




Highest Level LMAX defined by the hop distance
(D) a packet would travel at the bottom level.
DThi – Distance Threshold of level i.
If D > DThi , the packet is directed to level i+1.
Example: DThi = 6, 12, 20
Bottom Mesh Travel Distance (D)
LMAX
D>20
4
12<D≤20
3
6<D≤12
2
D≤6
1
How to distribute traffic
among hierarchy levels?
SHORTEST
PATH?
Shortest path –
light load
Average latencyHierarchical < Average latencyFlat
Shortest path –
heavy load
Congestion!!!
Shortest path,
but not for all?
The upper levels
are sparse!
Average latencyHierarchical >> Average latencyFlat
Shortest path only for distant
packets – heavy load
Average latencyHierarchical < Average latencyFlat
Shortest path only for distant
packets – light load
Traffic distribution – static vs.
dynamic
Traffic distribution
remains constant
Traffic Distribution is
adapted to the traffic
conditions
Dynamic traffic distribution –
Two modes
At light traffic loads:
Under heavy loads:
Example - 16x16 and 32x32
NoCs
Topology
16x16
[5,8]
[11,19]
32x32
[4,10,50]
[23,42,61]
Traffic Locality Model Bandwidth Version of Rent’s Rule
B – Cluster external bandwidth.
k – Average bandwidth per module.
G – Number of modules in a cluster.
R – Rent’s exponent, 0<R<1.
G = 16
B=∑
Greenfield et al., “Implications of Rent’s Rule for NoC Design and Its Fault-Tolerance”, NOCS 2007
Feedback
Average buffers
occupancy at the
bottleneck level among
the upper levels:
 Average  Buffers Occupancy  Level 2 



Feedback  max 


 Average  Buffers Occupancy  Level NL 


Feedback vs. injection rate
32x32, 4 Levels PyraMesh; Rentian traffic with R = 0.8
DTrD control scheme
Switch between distribution modes
using 2 feedback thresholds:
System architecture and
implementation costs
Logic:

Feedback logic : <10K
NAND gates.

Control logic : <1K gates.

Routing logic: comparable
to previous schemes.
Wires:

Feedback links of 4 wires
to <10% of the routers.

1 broadcast control bit to
all bottom mesh routers.
Communication:

1 mode bit in head flits.
Simulation set-up
Virtual channels per input port
2
Input buffer size [flits]
4
Packet size [flits]
8
Simulation clock period
2ns
Hierarchical NoC sizes
16x16, 32x32
Traffic Patterns
Rentian (R=0.6, 0.7, 0.8)
HNOCS – NoC simulation framework for OMNET++
http://hnocs.eew.technion.ac.il/
Yaniv Ben-Itzhak et. al., NOCS 2011
Average latency vs. injection
rate @ Rent’s exp. 0.6 - 0.8
Dynamic Simulation – 32x32
NoC
Conclusions
Static traffic distribution (STrD) in hierarchical NoCs
can optimize performance under either light or
heavy traffic loads, but not both at the same time.
Dynamic traffic distribution (DTrD) provides
optimal performance under both light and heavy
loads.
DTrD is lightweight, effective and feasible in future
systems with many thousands of modules.
DTrD is useful and desirable in any un-clustered
hierarchical NoC.
Thank You!