面向可重構(gòu)陣列的CNN多維融合數(shù)據(jù)復(fù)用方法

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中圖分類號(hào)：TP183 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1001-3695（2025）06-027-1801-06

doi：10.19734/j. issn.1001-3695.2024.10.0418

CNN multidimensional fusion data reuse method for reconfigurable arrays

Zhang Xiaofana?，Jiang Lin?，Li Yuancheng?，Sheng Mingweib （a.Colegeofia&foatinTooClfompuece&Tcology，Xi'UiestofSee logy，Xi’an710600，China）

Abstract：Reconfigurablearrayarchitecturescombinetheflexibilityof general-purposeprocesors withthehigh energyefficiencyof dedicated hardware，making themanidealsolution forcomputation-and memory-intensiveapplications suchasconvolutional neuralnetwork（CNN）.However，ncreasingcomputationaldemandsaisememoryacessoverhead，itingeficiencygains.Toaddressthis，thispaperproposeda CNN-orientedmultidimensionaldatareuse method forreconfigurablearrays.Byutilizing intra-computing-unitdata cyclicreuseandinter-unitdata pulsationtransfer，itachieveddatareuseacross bothcomputingunitsandarraydimensions.Taskswitching wasenabledthrougharryreconfigurationtofacilitatemultidimensional datareuse.Experimentalresultsonthe VirtexUltraScale440board showthatthis methodreduces memoryaccess byup to 69.4% ，improves computational speed byover 16.2% ，and achievesa computingunitutilizationrateof 94.1% .These results demonstrate thathis method enhances datareuseforCNNonreconfigurablearrys，enabling eficienthardware acceleration.

Key words：convolutional neural network；reconfigure structure；data reuse；paralel acceleration

0引言

近年來，隨著人工智能研究的興起，卷積神經(jīng)網(wǎng)絡(luò)（convo-lutionalneuralnetwork，CNN）憑借其出色的特征提取能力和良好的并行性，在圖像分類、目標(biāo)檢測(cè)等領(lǐng)域得到了廣泛應(yīng)用[1]。（剩余14882字）