Research on Test Scheduling of 3D NoC under Number Constraint of TSV (Through-Silicon-Vias) Using Evolution Algorithm Based on Cloud Model
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摘要: 針對硅通孔(TSV)價格昂貴、占用芯片面積大等問題，該文采用基于云模型的進化算法對TSV數(shù)量受約束的3維片上網(wǎng)絡(luò)(3D NoC)進行測試規(guī)劃研究，以優(yōu)化測試時間，并探討TSV的分配對3D NoC測試的影響，進一步優(yōu)化3D NoC在測試模式下的TSV數(shù)量。該方法將基于云模型的進化算法、小生境技術(shù)以及遺傳算法的雜交技術(shù)結(jié)合起來，有效運用遺傳、優(yōu)勝劣汰以及保持群落的多樣性等理念，以提高算法的尋優(yōu)速度和尋優(yōu)精度。研究結(jié)果表明，該算法既能有效避免陷入局部最優(yōu)解，又能提高全局尋優(yōu)能力和收斂速度，縮短了測試時間，并且優(yōu)化了3D NoC的測試TSV數(shù)量，提高了TSV的利用率。
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關(guān)鍵詞:
- 3維片上網(wǎng)絡(luò) /
- 硅通孔 /
- 云模型 /
- 進化算法
Abstract: As Through-Silicon-Vias (TSVs) in three-Dimensional Network-on-Chip (3D NoC) accompany some overhead such as the cost and the area, in order to optimize the number of TSVs of 3D NoC in test mode and reduce the test time, a new method using evolution algorithm based on cloud model is proposed to research on the test scheduling of 3D NoC and the impact of TSVs number and their allocation in each layer on 3D NoC test. This method combines the cloud evolution algorithm with niche technology and hybridization technique in genetic algorithm. It uses effectively the concepts of heredity, natural selection and community diversity to improve the quality of the algorithm on optimizing speed and precision. Experimental results demonstrate that the proposed method can not only effectively prevent from running into local optimization solution, but also improve the ability and speed of searching the best solution, and that TSVs number of 3D NoC can be optimized to improve the TSVs utilization. -
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