Open CL编译输出报告中给出了资源占用情况：

+--------------------------------------------------------------------+
; Estimated Resource Usage Summary                                   ;
+----------------------------------------+---------------------------+
; Resource                               + Usage                     ;
+----------------------------------------+---------------------------+
; Logic utilization                      ;   83%                     ;
; Dedicated logic registers              ;   46%                     ;
; Memory blocks                          ;   57%                     ;
; DSP blocks                             ;   25%                     ;
+----------------------------------------+---------------------------;

可见，逻辑资源、存储器资源消耗较为明显，而DSP资源并未用尽，说明还有优化的空间。

编译主程序需要使用SoCEDS，我用的版本为14.0.2.274，也是命令行方式，在工程目录下执行make，结束后得到可执行文件cnn。

将这两个文件拷贝到SD卡，按照前面的博客对板子进行设置，将CNN的模型、CIFAR10数据也拷贝到SD卡中，板子上电，mount SD卡到/mnt，执行cnn，得到输出如下：

<div class="blockcode"><blockquote>Please input the number of images(1~100):100
Loading data...OK!
Constructing CNN...OK!
Begin calculation...Elapsed Time = 141.861 s.
Real Label = 3(cat), Calc Label = 3(cat), error count = 0
Real Label = 8(ship), Calc Label = 8(ship), error count = 0
Real Label = 8(ship), Calc Label = 8(ship), error count = 0
Real Label = 0(airplane), Calc Label = 0(airplane), error count = 0
Real Label = 6(frog), Calc Label = 6(frog), error count = 0
Real Label = 6(frog), Calc Label = 6(frog), error count = 0
Real Label = 1(automobile), Calc Label = 1(automobile), error count = 0
Real Label = 6(frog), Calc Label = 6(frog), error count = 0
Real Label = 3(cat), Calc Label = 3(cat), error count = 0
Real Label = 1(automobile), Calc Label = 1(automobile), error count = 0
Real Label = 0(airplane), Calc Label = 0(airplane), error count = 0
Real Label = 9(truck), Calc Label = 9(truck), error count = 0
Real Label = 5(dog), Calc Label = 5(dog), error count = 0
Real Label = 7(horse), Calc Label = 7(horse), error count = 0
Real Label = 9(truck), Calc Label = 9(truck), error count = 0
Real Label = 8(ship), Calc Label = 8(ship), error count = 0
Real Label = 5(dog), Calc Label = 5(dog), error count = 0
Real Label = 7(horse), Calc Label = 7(horse), error count = 0
Real Label = 8(ship), Calc Label = 8(ship), error count = 0
Real Label = 6(frog), Calc Label = 6(frog), error count = 0
Real Label = 7(horse), Calc Label = 7(horse), error count = 0
Real Label = 0(airplane), Calc Label = 2(bird), error count = 1
Real Label = 4(deer), Calc Label = 4(deer), error count = 1
Real Label = 9(truck), Calc Label = 9(truck), error count = 1
Real Label = 5(dog), Calc Label = 4(deer), error count = 2
Real Label = 2(bird), Calc Label = 3(cat), error count = 3
Real Label = 4(deer), Calc Label = 4(deer), error count = 3
Real Label = 0(airplane), Calc Label = 0(airplane), error count = 3
Real Label = 9(truck), Calc Label = 9(truck), error count = 3
Real Label = 6(frog), Calc Label = 6(frog), error count = 3
Real Label = 6(frog), Calc Label = 6(frog), error count = 3
Real Label = 5(dog), Calc Label = 5(dog), error count = 3
Real Label = 4(deer), Calc Label = 4(deer), error count = 3
Real Label = 5(dog), Calc Label = 5(dog), error count = 3
Real Label = 9(truck), Calc Label = 9(truck), error count = 3
Real Label = 2(bird), Calc Label = 3(cat), error count = 4
Real Label = 4(deer), Calc Label = 7(horse), error count = 5
Real Label = 1(automobile), Calc Label = 9(truck), error count = 6
Real Label = 9(truck), Calc Label = 9(truck), error count = 6
Real Label = 5(dog), Calc Label = 5(dog), error count = 6
Real Label = 4(deer), Calc Label = 4(deer), error count = 6
Real Label = 6(frog), Calc Label = 6(frog), error count = 6
Real Label = 5(dog), Calc Label = 5(dog), error count = 6
Real Label = 6(frog), Calc Label = 6(frog), error count = 6
Real Label = 0(airplane), Calc Label = 0(airplane), error count = 6
Real Label = 9(truck), Calc Label = 9(truck), error count = 6
Real Label = 3(cat), Calc Label = 5(dog), error count = 7
Real Label = 9(truck), Calc Label = 9(truck), error count = 7
Real Label = 7(horse), Calc Label = 7(horse), error count = 7
Real Label = 6(frog), Calc Label = 6(frog), error count = 7
Real Label = 9(truck), Calc Label = 9(truck), error count = 7
Real Label = 8(ship), Calc Label = 8(ship), error count = 7
Real Label = 0(airplane), Calc Label = 2(bird), error count = 8
Real Label = 3(cat), Calc Label = 3(cat), error count = 8
Real Label = 8(ship), Calc Label = 8(ship), error count = 8
Real Label = 8(ship), Calc Label = 8(ship), error count = 8
Real Label = 7(horse), Calc Label = 7(horse), error count = 8
Real Label = 7(horse), Calc Label = 7(horse), error count = 8
Real Label = 4(deer), Calc Label = 3(cat), error count = 9
Real Label = 6(frog), Calc Label = 3(cat), error count = 10
Real Label = 7(horse), Calc Label = 7(horse), error count = 10
Real Label = 3(cat), Calc Label = 5(dog), error count = 11
Real Label = 6(frog), Calc Label = 6(frog), error count = 11
Real Label = 3(cat), Calc Label = 3(cat), error count = 11
Real Label = 6(frog), Calc Label = 6(frog), error count = 11
Real Label = 2(bird), Calc Label = 2(bird), error count = 11
Real Label = 1(automobile), Calc Label = 1(automobile), error count = 11
Real Label = 2(bird), Calc Label = 2(bird), error count = 11
Real Label = 3(cat), Calc Label = 3(cat), error count = 11
Real Label = 7(horse), Calc Label = 9(truck), error count = 12
Real Label = 2(bird), Calc Label = 2(bird), error count = 12
Real Label = 6(frog), Calc Label = 6(frog), error count = 12
Real Label = 8(ship), Calc Label = 8(ship), error count = 12
Real Label = 8(ship), Calc Label = 8(ship), error count = 12
Real Label = 0(airplane), Calc Label = 0(airplane), error count = 12
Real Label = 2(bird), Calc Label = 2(bird), error count = 12
Real Label = 9(truck), Calc Label = 0(airplane), error count = 13
Real Label = 3(cat), Calc Label = 3(cat), error count = 13
Real Label = 3(cat), Calc Label = 2(bird), error count = 14
Real Label = 8(ship), Calc Label = 8(ship), error count = 14
Real Label = 8(ship), Calc Label = 8(ship), error count = 14
Real Label = 1(automobile), Calc Label = 1(automobile), error count = 14
Real Label = 1(automobile), Calc Label = 1(automobile), error count = 14
Real Label = 7(horse), Calc Label = 7(horse), error count = 14
Real Label = 2(bird), Calc Label = 2(bird), error count = 14
Real Label = 5(dog), Calc Label = 7(horse), error count = 15
Real Label = 2(bird), Calc Label = 2(bird), error count = 15
Real Label = 7(horse), Calc Label = 7(horse), error count = 15
Real Label = 8(ship), Calc Label = 8(ship), error count = 15
Real Label = 9(truck), Calc Label = 9(truck), error count = 15
Real Label = 0(airplane), Calc Label = 0(airplane), error count = 15
Real Label = 3(cat), Calc Label = 4(deer), error count = 16
Real Label = 8(ship), Calc Label = 8(ship), error count = 16
Real Label = 6(frog), Calc Label = 6(frog), error count = 16
Real Label = 4(deer), Calc Label = 4(deer), error count = 16
Real Label = 6(frog), Calc Label = 6(frog), error count = 16
Real Label = 6(frog), Calc Label = 6(frog), error count = 16
Real Label = 0(airplane), Calc Label = 2(bird), error count = 17
Real Label = 0(airplane), Calc Label = 0(airplane), error count = 17
Real Label = 7(horse), Calc Label = 7(horse), error count = 17
Classify Score = 83 %.

上面的执行流程是这样的，首先输入测试样本数目（1到100），由于DE1板子FPGA端SDRAM容量较小，难以加载全部测试数据（10000张图片），故每次最多装入100张图片。之后载入数据到HPS内存，然后开始构建CNN模型，构建过程中也实现了Open CL的初始化。构建完毕，将输入图像依次通过CNN，得到一系列分类结果，与标签进行对比，统计错误分类个数，计算分类准确率。

经过测试，分类准确率达到83%，与Caffe测试结果一致。

经过以上测试，可以得到结论：

（1）使用Open CL可以很方便地移植高级语言编写的算法；

（2）CNN在移植过程中需要考虑实际硬件，定制合适的模型和数据；

（3）Cyclone 5逻辑资源较少（85K，Open CL kernel占用了83%），如果希望进一步提高计算速度，一方面可以选用高性能器件（如Stratix V、Arria 10），另一方面可以使用RTL自己搭建计算系统。

我目前正在Stratix V上用RTL搭建并行+流水线CNN加速器，有兴趣的童鞋可以进一步交流。

码字不易，申请加精。


（完结）