1. Edge-centric Modulo Scheduling for Coarse-Grained Reconfigurable Architectures
Hyunchul Park†, Kevin Fan†, Scott Mahlke†,
Taewook Oh‡, Heeseok Kim‡, Hong-seok Kim‡
† University of Michigan
‡ Samsung Advanced Institute of Technology
October 28, 2008 1

2. Coarse-Grained Reconfigurable Architecture (CGRA)
Array of PEs connected in a mesh-like interconnect
High throughput with a large number of resources
Distributed hardware offers low cost/power consumption
High flexibility with dynamic reconfiguration 2

3. CGRA : Attractive Alternative to ASICs
Suitable for running multimedia applications for future embedded systems
High throughput, low power consumption, high flexibility
Morphosys : 8x8 array with RISC processor
SiliconHive : hierarchical systolic array
ADRES : 4x4 array with tightly coupled VLIW
Morphosys SiliconHive ADRES
viterbi at 80Mbps
h.264 at 30fps
50-60 MOps /mW 3

4. Scheduling in CGRA Sparse interconnect and distributed register files
No dedicated routing resources : FUs are used for routing
Need explicit routing of operands by compiler FU RF FU RF FU RF FU RF
Central RF FU RF FU RF FU RF FU RF FU FU FU FU FU RF FU RF FU RF FU RF
Conventional VLIW FU RF FU RF FU RF FU RF
CGRA 4

5. Scheduling Difficulties
VLIW : routing is guaranteed by central RF
CGRA : Multiple possible routes
Compiler is responsible for finding routes
Routing can easily fail by other operations
time
time
VLIW
CGRA 5

6. Objective of This Work Modulo scheduling technique for CGRAs
Exploit loop-level parallelism by overlapping execution of iterations
Customized approach based on characteristics of CGRAs
Achieve fast compile time and good performance
Huge scheduling space, distributed resources
Naïve approach can result in either poor solution or long compile time 6

7. Traditional Approach : Node-centric
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4 P1 P2 C P1 P2 C C 1 C C C 2 3 4 C C C 5 6 7
FU 0
FU 1
FU 2
FU 3
FU 4 C C C 8 9 10
Operations are placed first, then routing is performed
Visit all candidate slots to find the solution 7

8. Node-centric Inefficiency 1
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4 P1 P2 C P1 P2 C 1 C C 2 3 4 C C 5 6 7
FU 0
FU 1
FU 2
FU 3
FU 4 C 8 9 10
Attempt routing to non-reachable slots by edge P1 to C 8

9. Node-centric Inefficiency 2
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4 P1 P2 P1 P2 C C 1 C 2 3 4 C 5 6 7
FU 0
FU 1
FU 2
FU 3
FU 4 C 8 9 10
Repeat the same routing already performed 9

10. Our Approach : Edge-centric
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4 P1 P2 P1 P2 C 1 C 2 3 4 1 C 5 6 7 2 C C C 8 9 10 3 4
Node-centric
Edge-centric
Start routing without placing the operation
Placement occurs during routing 10

11. Benefit 1 : Less Routing Calls
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4 P1 P2 P1 P2 1 2 3 4 1 5 6 7 2 C C 8 9 10 3 4
Node-centric
Edge-centric
11 routing calls for P1  C
1 routing call for P1  C
Reduce compile time with less number of routing calls 11

12. Benefit 2 : Global View
node-centric
edge-centric
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4 P P 10 1 C 1 1 C C 2 2
Assume slot 0 is a precious resource (better to save it for later use)
Node-centric greedily picks slot 1
Edge-centric can avoid slot 0 by simply assigning a high cost 12

13. Edge-centric Modulo Scheduling
It’s all about edges
Scheduling is constructed by routing ‘edges’
Placement is integrated into routing process
Global perspective for EMS
Scheduling order of edges
Prioritize edges to determine scheduling order
Routing optimization
Develop contention model for routing resources 13

14. 1: Edge Prioritization Focus on # consumers Height-based priority
Simple edges / High fanout edges
Height-based priority
Give high priority to high fanout edges
Edges scheduled later will likely use extra resources
Extra resources in simple edges are just being wasted
Extra resources in high-fanout edges can be helpful
Other consumers can make use of those 14

15. Fanout Clustering Our approach : the opposite
Give priority to simple edges
Operations connected in simple edges form a cluster
Schedule simple edges within a cluster
Schedule high-fanout edges when consumers are visited
17 of 81 loops in H.264 show better throughput
Only 1 shows worse throughput 15

16. 2: Routing Optimization
Routing is guided by cost associated with each routing slot
Intelligent routing cost metrics are important
Minimize # routing resources for current edge
Static cost : fixed positive cost for each resource
Minimize # routing resources for other edges to prod/cons
Affinity cost : use common consumer information
Avoid routing failures for other edges
Probabilistic cost : predict future resource usage
routing cost = F(static cost, affinity cost, probabilistic cost) 16

17. Affinity Cost Heuristic
time
FU 0
FU 1
FU 2
FU 3 1 2 3
time
FU 0
FU 1
FU 2
FU 3 1 2 3 A B C A B A B C C
FU 0
FU 1
FU 2
FU 3
Routing Cost = 2
Routing Cost = 0
Affinity cost : utilize common consumer information
Affinity value : how close common consumer is in DFG
Place operations with high affinity close to each other 17

18. Probabilistic Cost Heuristic
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4 5 6 7 P1 P2 P1 C1 C2 P2 . C1 C2 ST
Three possible routes, all using same # routing slots 18

19. Probabilistic Cost Heuritsic
time
FU 0
FU 1
FU 2
FU 3
FU 4 1 2 3 4 5 6 7 P1 P2 P1 ST C1 C2 P2 . C1 C2 ST
Need to consider other unplaced edges/operations
Slots that might be used for routing P2  C2
Slots that might be used for placing ST 19

20. Probabilistic Cost Heuritsic
time
FU 0
FU 1
FU 2
FU 3
FU 4
0.33 1 2
1.0 3 4 5
0.5 6 7 P1 P2 P1 C1 C2 P2 . X X C1 C2 ST
Probabilities on future usage of slots are calculated
and guide routing of P1  C1
Route in the middle is selected 20

21. EMS System Flow Schedule Preprocessing DFG CGRA Select target edge
Cost calculation
Fanout clustering
Final schedule
DFG
Perform routing
Prioritize edges
Place operations
Route to others
CGRA 21

22. Experimental Setup 214 loops from highly optimized media applications
H.264, 3D graphics, AAC, MP3
Target architecture
4x4 heterogeneous CGRA (6 memory, 4 multiply)
Local RF for each PE
Mesh-plus interconnect : mesh + 2 hop connections
Compared to 3 other solutions
IMS : iterative modulo scheduling, no routing optimization
NMS : same heuristics as EMS, but in a node-centric way
DRESC : IMEC’s simulated annealing 22

23. Results Performance : normalized throughput of loops
+10%
0.5x
+24% 2x
-2%
-18x
Performance : normalized throughput of loops
Max throughput is determined by # ops in a loop and # resources
Compile time : for all 214 loops 23

24. Conclusion EMS is a good match for scheduling in CGRA
Routing is more important than placement
Edge-centric approach allows fast compile time
18x speed up over simulated annealing
Intelligent routing cost metrics allows good performance
24% improvement over IMS, 98% performance of existing solution 24

25. Questions ? 25