SCIENCE CHINA Information Sciences, Volume 61, Issue 6: 060415(2018) https://doi.org/10.1007/s11432-017-9351-4

## An intelligent partitioning approach of the system-on-chip for flexible and stretchable systems

• ReceivedOct 27, 2017
• AcceptedJan 11, 2018
• PublishedApr 19, 2018
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### Abstract

In this paper, we propose an intelligent partitioning approach of the system-on-chip (SoC) to improve the bendability and stretchability of flexible and stretchable systems. The proposed approach partitions the SoC intelligently into clusters of functional modules according to the communication flows and area constraint. Based on the communication volume between clusters, a heuristic algorithm is applied to map these clusters onto the 2D mesh network-on-chip (NoC) for co-optimization of communication energy and delay. Experimental results show that our approach can effectively partition the SoC into small ICs of the same size. The approach also reduces power consumption and communication delay by 10.64%–56.63% and 15.06%–50.30%, respectively.

### Acknowledgment

This work was supported by National Basic Research Program of China (Grant No. 2015CB351906), National Natural Science Foundation of China (Grant No. 61172030), and Programme of Introducing Talents of Discipline to Universities (111 Project) (Grant No. B12026).

### References

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• Figure 1

Illustration of flexible and stretchable systems with integrated sensing, computing and communication capabilities.

• Figure 2

(Color online) Examples of bendable or stretchable systems. (a) Forces are applied in the $y$ direction only;protect łinebreak (b) forces are applied in the $y$ and $z$ directions only.

• Figure 3

Illustration of flexible and stretchable systems based on network-on-chip.

• Figure 4

(Color online) Examples of bendable or stretchable systems. (a) Forces are applied in the $y$ direction only;protectłinebreak (b) forces are applied in the $y$ and $z$ direction only.

• Figure 5

Overview of the proposed partitioning approach of the SoC.

• Figure 6

(Color online) Examples of (a) functional module characteristic graph, (b) cluster communication graph, and (c) architecture characterization graph.

•

Algorithm 1 Function module clustering

Require:The application characteristics graph (FMCG) of the given SoC $G({\rm~FM},~A)$, the number of clusters $N_{\rm~cluster}$, and the area of the functional module $S({\rm~fm}_i)$;

Output:A cluster communication graph (CCG) $G(C,F)$.

Calculate the average area of clusters $S_{\rm~avg}$ based on $N_{\rm~cluste}$;

Based on the communication volume, sort the functional modules in descending order;

Set the first functional module in the sort as cluster head;

for $i=1$; $i<N_{\rm~cluster}$; $i++$

while $(S(c_{i})~<~S_{\rm~avg})$ do

if $(\exists$ adjacent functional module $~\notin~$ $c_{i})$ then

Put an adjacent functional module in $c_i$;

else

Select another functional module from $c_i$ as the new cluster head;

end if

end while

Select a new cluster head from the rest of the functional modules;

end for

Calculate the communication volume between clusters $F$;

Obtain the CCG $G(C,F)$.

• Table 1   Genetic operators in the operator pool
 Selection Crossover Mutation S1: Truncation selection C1: Discrete recombination M1: Random mutation S2: Tournament selection C2: Single point recombination M2: Swap mutation S3: Roulette wheel selection C3: Multi-point recombination M3: Discrete breeder mutation
• Table 2   Effectiveness of the proposed functional module clustering algorithm
 Benchmark $V$ $E$ No. of clusters No clusters [3] [20] Proposed VA CV Area VA CV Area $t$ (s) VA CV Area $t$ (s) SoC25 25 35 8 7.05 822 225 5.91 535 138 0.05 2.94 309 130 0.01 SoC26 26 62 6 55.09 5480 624 55.47 2160 437 0.10 7.47 2320 396 0.04 SoC38 38 47 12 10.14 11892 608 18.70 4101 342 0.12 9.13 5053 336 0.04 SoC50 50 147 9 4.39 37457 425 6.44 27209 387 0.18 0.86 23099 360 0.07 SoC80 80 292 9 20.78 78992 1280 12.54 59325 1161 1.20 7.43 51332 1071 0.40
•

Algorithm 2 Cluster mapping

Require:Cluster communication graph CCG, architecture characterization graph ARCG and routing algorithm;

Output:Position of mapped clusters.

Initialize the parent population;

Calculate the fitness of the parent population;

while (not termination condition) do

Select operators from the operator pool by roulette wheel selection;

Obtain the offspring population from the selected genetic operators;

Calculate the fitness of the offspring population;

Update the weights of operators based on the “reward” mechanism;

Compare the parent and offspring populations based on fitness;

Generate a new parent population;

end while

Obtain the best individual as the mapping result.

• Table 3   Average runtime of the mapping algorithms
 Benchmark No. of clusters SA[21] PSA[22] GA[23] GMO C_SoC25 8 11.26 14.06 6.19 1.13 C_SoC26 6 0.07 0.05 0.04 0.04 C_SoC38 12 67.81 84.66 108.12 27.38 C_SoC50 9 17.70 24.05 24.04 8.98 C_SoC80 9 23.18 33.35 23.45 8.93
• Table 4   Improvements in communication energy and delay achieved by the mapping algorithms
 Benchmark SA[21] PSA[22] GA[23] GMO $E$ (%) $D$ (%) $E$ (%) $D$ (%) $E$ (%) $D$ (%) $E$ (%) $D$ (%) C_SoC25 56.63 41.08 56.63 41.08 56.63 41.08 56.63 41.08 C_SoC26 24.28 36.62 24.28 36.62 24.28 36.62 24.28 36.62 C_SoC38 35.85 48.76 35.65 49.78 35.28 49.91 36.45 50.30 C_SoC50 12.91 16.35 13.34 16.60 12.78 15.60 13.91 18.35 C_SoC80 10.10 14.57 10.34 14.06 9.21 14.43 10.64 15.06

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