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).
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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.
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; |
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Put an adjacent functional module in $c_i$; |
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Select another functional module from $c_i$ as the new cluster head; |
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Select a new cluster head from the rest of the functional modules; |
Calculate the communication volume between clusters $F$; |
Obtain the CCG $G(C,F)$. |
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 |
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 |
Initialize the parent population; |
Calculate the fitness of the parent population; |
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; |
Obtain the best individual as the mapping result. |
Benchmark | No. of clusters | SA | PSA | GA | 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 |
Benchmark | SA | PSA | GA | 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 |