lyu 发表于 2024-1-22 14:34:27

IAR 仿真很慢的问题

rt:

使用的IAR 9.50.1的编译器
data:image/png;base64,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
芯片使用的fpga模拟R5内核 参考硬汉哥从零构建的
在仿真的时候出现了一个问题,使用freeRTOS时,各个任务总是互相卡住。
后来定位发现是由于仿真信息不同步导致的。仿真的时候十分的慢,大概1ms = 几秒。
但是仿真断开后板子又可以正常运行。各项计数器很正常
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
仿真时各个计数器就变得巨慢
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
由于用的是FPGA,boot早都烧写好了,频率是100Mhz,我不知道仿真频率是多少,不知道能不能设置它,另外,使用了GIC中断,他的时钟是独立于CPU的。
想仿真起来正常一些,调试比较容易

eric2013 发表于 2024-1-22 14:40:32

这个IP核是不是ARM提供的,如果ARM提供的,应该还行。有没有测试过你这个调试接口实际读写速度是多少。

lyu 发表于 2024-1-26 10:33:33

做了几个验证了,应该不是速度的问题,可能处在swi 的软件中断那块,或者上下文之类的地方。1、使用SEGGER的环境+jlink-jtag调试器。仿真速度还是正常的。2、用IAR环境+jlink-jtag调试,如果只做一个简单的timer中断,仿真速度也是正常的。3、IAR环境+jlink-jtag+FreeRTOS,这时候全速仿真(不涉及到ISR的信号量),在1ms的中断中就变的异常了(变为大概36ms)。3、不仿真的时候,两个环境都正常了。反正不仿真的时候也能用,现在感觉有两个原因,1、就是jlink是segger官方出的调试器,手里也没其他调试器了。2、我们调试阶段的debug仲裁这一套机制会不会有问题,不太熟悉这里,这种比较关键的IP都是买的。
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查看完整版本: IAR 仿真很慢的问题