191 lines
9.8 KiB
Markdown
191 lines
9.8 KiB
Markdown
# RK3588 NPU SRAM使用说明
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* RK3588 SOC内部含有1MB的SRAM，其中有956KB可供给SOC上各个IP所使用，已支持为RKNPU指定分配使用
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* SRAM可以帮助RKNPU应用减轻DDR带宽压力，目前支持为Internal和Weight两种类型内存指定分配SRAM
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---
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一、板端环境要求
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---
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1、内核环境要求
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* RKNPU驱动版本>=0.8.0
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* 内核config需要开启CONFIG_ROCKCHIP_RKNPU_SRAM=y
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* Android系统config路径如下：
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```shell
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<path-to-your-kernel>/arch/arm64/configs/rockchip_defconfig
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```
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* Linux系统config路径如下：
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```
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<path-to-your-kernel>/arch/arm64/configs/rockchip_linux_defconfig
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```
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* 内核相应DTS需要从系统SRAM中分配给RKNPU使用
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* 从系统分配需求大小的SRAM给RKNPU，最大可分配956KB，且大小需要4K对齐
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* 注意：默认系统中可能已为其他IP分配SRAM，比如编解码模块，各IP分配的SRAM区域不能重叠，否则会存在同时读写出现数据错乱现象
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* 如下为956KB全部分配给RKNPU的例子：
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```dts
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syssram: sram@ff001000 {
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compatible = "mmio-sram";
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reg = <0x0 0xff001000 0x0 0xef000>;
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#address-cells = <1>;
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#size-cells = <1>;
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ranges = <0x0 0x0 0xff001000 0xef000>;
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/* 分配RKNPU SRAM */
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/* start address and size should be 4k algin */
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rknpu_sram: rknpu_sram@0 {
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reg = <0x0 0xef000>; // 956KB
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};
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};
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```
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* 把分配的SRAM挂到RKNPU节点，修改如下所示的dtsi文件：
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```shell
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<path-to-your-kernel>/arch/arm64/boot/dts/rockchip/rk3588s.dtsi
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```
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```dts
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rknpu: npu@fdab0000 {
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compatible = "rockchip,rk3588-rknpu";
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/* ... */
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/* 增加RKNPU sram的引用 */
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rockchip,sram = <&rknpu_sram>;
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status = "disabled";
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};
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```
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2、RKNN SDK版本要求
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* RKNPU Runtime库(librknnrt.so)版本>=1.3.4b14
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---
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二、使用方法
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---
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1、指定Internal使用SRAM:
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* 自动大小方式，将尝试从系统分配剩余足够的SRAM给Internal使用
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* **export RKNN_INTERNAL_MEM_TYPE=sram**
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* 指定大小方式，将尝试从系统分配指定256KB大小的SRAM给Internal使用
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* **export RKNN_INTERNAL_MEM_TYPE=sram#256**
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2、指定Weight使用SRAM：
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* 自动大小方式，将尝试从系统分配剩余足够的SRAM给Weight使用
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* **export RKNN_SEPARATE_WEIGHT_MEM=1**
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* **export RKNN_WEIGHT_MEM_TYPE=sram**
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* 指定大小方式，将尝试从系统分配指定128KB大小的SRAM给Weight使用
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* **export RKNN_SEPARATE_WEIGHT_MEM=1**
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* **export RKNN_WEIGHT_MEM_TYPE=sram#128**
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3、混合指定
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* RKNPU驱动支持对SRAM内存管理，支持同时指定SRAM给Internal和Weight同时使用，如下：
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* **export RKNN_INTERNAL_MEM_TYPE=sram#256**
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* **export RKNN_SEPARATE_WEIGHT_MEM=1**
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* **export RKNN_WEIGHT_MEM_TYPE=sram#128**
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---
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三、调试方法
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---
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1、SRAM是否启用查询
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* 通过开机串口日志查看SRAM是否启用，包含为RKNPU指定SRAM的地址范围和大小信息，如下所示：
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```shell
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rk3588_s:/ # dmesg | grep rknpu -i
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RKNPU fdab0000.npu: RKNPU: sram region: [0x00000000ff001000, 0x00000000ff0f0000), sram size: 0xef000
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```
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2、SRAM使用情况查询
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* 可通过节点查询SRAM的使用情况
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* 如下为未使用SRAM的位图表，每个点表示4K大小
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```shell
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rk3588_s:/ # cat /sys/kernel/debug/rknpu/mm
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SRAM bitmap: "*" - used, "." - free (1bit = 4KB)
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[000] [................................]
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[001] [................................]
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[002] [................................]
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[003] [................................]
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[004] [................................]
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[005] [................................]
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[006] [................................]
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[007] [...............]
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SRAM total size: 978944, used: 0, free: 978944
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```
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* 如下为分配使用512KB后的SRAM位图表
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```shell
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rk3588_s:/ # cat /sys/kernel/debug/rknpu/mm
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SRAM bitmap: "*" - used, "." - free (1bit = 4KB)
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[000] [********************************]
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[001] [********************************]
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[002] [********************************]
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[003] [********************************]
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[004] [................................]
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[005] [................................]
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[006] [................................]
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[007] [...............]
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SRAM total size: 978944, used: 524288, free: 454656
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```
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3、通过RKNN API查询SRAM大小
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* 通过rknn_query的RKNN_QUERY_MEM_SIZE接口查询SRAM大小信息
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```C++
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typedef struct _rknn_mem_size {
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uint32_t total_weight_size;
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uint32_t total_internal_size;
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uint64_t total_dma_allocated_size;
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uint32_t total_sram_size;
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uint32_t free_sram_size;
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uint32_t reserved[10];
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} rknn_mem_size;
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```
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* 其中，total_sram_size表示：系统给RKNPU分配的SRAM总大小
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* free_sram_size表示：剩余RKNPU能使用的SRAM大小
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4、查看网络SRAM的占用情况
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* 板端环境中，RKNN应用运行前设置如下环境变量，可打印SRAM使用预测情况：
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```shell
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export RKNN_LOG_LEVEL=3
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```
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* Internal分配SRAM的逐层占用情况，如下日志所示：
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```shell
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---------------------------------------------------------------------------
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Total allocated Internal SRAM Size: 524288, Addr: [0xff3e0000, 0xff460000)
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---------------------------------------------------------------------------
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---------------------------------------------------------------------+----------------------------------+-----------
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ID User Tensor DataType OrigShape NativeShape | [Start End) Size | SramHit
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---------------------------------------------------------------------+----------------------------------+-----------
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1 ConvRelu input0 INT8 (1,3,224,224) (1,1,224,224,3) | 0xff3b0000 0xff3d4c00 0x00024c00 | \
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2 ConvRelu output2 INT8 (1,32,112,112) (1,2,112,112,16) | 0xff404c00 0xff466c00 0x00062000 | 0x0005b400
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3 ConvRelu output4 INT8 (1,32,112,112) (1,4,112,112,16) | 0xff466c00 0xff52ac00 0x000c4000 | 0x00000000
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4 ConvRelu output6 INT8 (1,64,112,112) (1,4,112,112,16) | 0xff52ac00*0xff5eec00 0x000c4000 | 0x00000000
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5 ConvRelu output8 INT8 (1,64,56,56) (1,4,56,56,16) | 0xff3e0000 0xff411000 0x00031000 | 0x00031000
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6 ConvRelu output10 INT8 (1,128,56,56) (1,8,56,56,16) | 0xff411000 0xff473000 0x00062000 | 0x0004f000
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7 ConvRelu output12 INT8 (1,128,56,56) (1,8,56,56,16) | 0xff473000 0xff4d5000 0x00062000 | 0x00000000
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8 ConvRelu output14 INT8 (1,128,56,56) (1,8,56,56,16) | 0xff3e0000 0xff442000 0x00062000 | 0x00062000
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9 ConvRelu output16 INT8 (1,128,28,28) (1,8,28,28,16) | 0xff442000 0xff45a800 0x00018800 | 0x00018800
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10 ConvRelu output18 INT8 (1,256,28,28) (1,16,28,28,16) | 0xff3e0000 0xff411000 0x00031000 | 0x00031000
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11 ConvRelu output20 INT8 (1,256,28,28) (1,16,28,28,16) | 0xff411000 0xff442000 0x00031000 | 0x00031000
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12 ConvRelu output22 INT8 (1,256,28,28) (1,16,28,28,16) | 0xff3e0000 0xff411000 0x00031000 | 0x00031000
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13 ConvRelu output24 INT8 (1,256,14,14) (1,16,14,14,16) | 0xff411000 0xff41d400 0x0000c400 | 0x0000c400
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14 ConvRelu output26 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3e0000 0xff3f8800 0x00018800 | 0x00018800
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15 ConvRelu output28 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3f8800 0xff411000 0x00018800 | 0x00018800
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16 ConvRelu output30 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3e0000 0xff3f8800 0x00018800 | 0x00018800
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17 ConvRelu output32 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3f8800 0xff411000 0x00018800 | 0x00018800
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18 ConvRelu output34 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3e0000 0xff3f8800 0x00018800 | 0x00018800
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19 ConvRelu output36 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3f8800 0xff411000 0x00018800 | 0x00018800
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20 ConvRelu output38 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3e0000 0xff3f8800 0x00018800 | 0x00018800
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21 ConvRelu output40 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3f8800 0xff411000 0x00018800 | 0x00018800
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22 ConvRelu output42 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3e0000 0xff3f8800 0x00018800 | 0x00018800
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23 ConvRelu output44 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3f8800 0xff411000 0x00018800 | 0x00018800
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24 ConvRelu output46 INT8 (1,512,14,14) (1,32,14,14,16) | 0xff3e0000 0xff3f8800 0x00018800 | 0x00018800
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25 ConvRelu output48 INT8 (1,512,7,7) (1,33,7,7,16) | 0xff3f8800 0xff3ff000 0x00006800 | 0x00006800
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26 ConvRelu output50 INT8 (1,1024,7,7) (1,67,7,7,16) | 0xff3e0000 0xff3ed000 0x0000d000 | 0x0000d000
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27 ConvRelu output52 INT8 (1,1024,7,7) (1,67,7,7,16) | 0xff3ed000 0xff3fa000 0x0000d000 | 0x0000d000
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28 AveragePool output54 INT8 (1,1024,7,7) (1,67,7,7,16) | 0xff3e0000 0xff3ed000 0x0000d000 | 0x0000d000
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29 Conv output55 INT8 (1,1024,1,1) (1,64,1,1,16) | 0xff3ed000 0xff3ed400 0x00000400 | 0x00000400
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30 Softmax output56 INT8 (1,1000,1,1) (1,64,1,1,16) | 0xff3e0000 0xff3e0400 0x00000400 | 0x00000400
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31 OutputOperator output57 FLOAT (1,1000,1,1) (1,1000,1,1) | 0xff3ae000 0xff3aefa0 0x00000fa0 | \
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---------------------------------------------------------------------+----------------------------------+-----------
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----------------------------------------
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Total Weight Memory Size: 4260864
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Total Internal Memory Size: 2157568
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Predict Internal Memory RW Amount: 11068320
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Predict Weight Memory RW Amount: 4260832
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Predict SRAM Hit RW Amount: 6688768
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----------------------------------------
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```
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* 其中上面文本图表中的SramHit为当前层Tensor所占用的SRAM大小，一般情况下将会节省当前大小的读+写的带宽
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* Predict SRAM Hit RW Amount表示整个网络SRAM的读写预测情况，可近似估计每帧节省的带宽
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* 注意：Linux环境日志重定向到终端，Android环境日志重定向到logcat
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