This tutorial uses TinkerBoard 2 series, an RK3399 Arm-based single board computer (SBC) with a 64-bit processor using Arm big.LITTLE™ technology to deliver enhanced computing performance at lower power consumption. The 6-core Rockchip RK3399 System-on-Chip (S...
Clang appears to produce noticeably faster CPU inference than GCC for POWER9 targets. For fastest inference, use Clang 18 or higher; earlier versions of Clang may have impaired inference speed due toBug 49864andBug 64664.
显存上传前和下载后的 fp32 fp16转换是在cpu上执行的,这部分逻辑也使用了openmp。 解决方法 1. 绑核 如果使用有大小核cpu的设备,建议通过ncnn::set_cpu_powersave(int)绑定大核或小核,注意windows系统不支持绑核。顺便说一下,ncnn支持不同的模型运行在不同的核心。假设硬件平台有2个大核,4个小核,你想把...
ncnn::set_cpu_powersave(powersave);ncnn::set_omp_dynamic(0);11 changes: 9 additions & 2 deletions 11 src/gpu.cpp Original file line numberDiff line numberDiff line change @@ -543,6 +543,7 @@ int create_gpu_instance() // NCNN_LOGE("[%u] pipelineCacheUUID = %u", i, ...
The backend operators are primely optimized to make the best use of computing power in different architectures, regarding instruction issue, throughput, delay, cache bandwidth, cache delay, registers, etc.. The TNN performance on mainstream hardware platforms (CPU: ARMv7, ARMv8, X86, GPU: Mali,...
The backend operators are primely optimized to make the best use of computing power in different architectures, regarding instruction issue, throughput, delay, cache bandwidth, cache delay, registers, etc.. The TNN performance on mainstream hardware platforms (CPU: ARMv7, ARMv8, X86, GPU: Mali,...
_cpu_powersave2); ncnn::set_omp__threadsncnn::get_big_cpu_count(); yoloopt = ncnn::Option(); //#ifNCNN_VULKAN // yolo.opt.use_vulkan_compute = use_gpu; //#endif yolo.opt.num_threads= ncnn::get_bigcpu_count(); yolo.opt.blob_allocator = &_pool_; yolo.opt....
使用NCNN标准的多线程配置: ncnn:Net *net = new ncnn::Net(); ncnn::set_cpu_powersave(2); ncnn::set_omp_num_threads(ncnn::get_big_cpu_count()); net->opt.num_threads = ncnn::get_big_cpu_count(); 然后分别在骁龙8gen3和Intel i5-12600k上测试一个改造过的mobilenet网络,结果如下 ...
~/ncnn# ./benchncnn 4 1 0 -1 1 loop_count = 4 num_threads = 1 powersave = 0 gpu_device = -1 cooling_down = 1 squeezenet min = 143.97 max = 144.90 avg = 144.28 squeezenet_int8 min = 130.06 max = 130.78 avg = 130.35 mobilenet min = 225.67 max = 227.60 avg = 226.25 ...
# ./benchncnn 4 2 0 -1 0 loop_count = 4 num_threads = 2 powersave = 0 gpu_device = -1 cooling_down = 0 FastestDet min = 191.45 max = 193.48 avg = 192.35 0x4 开启bf16加速 这款开发板内存较小,同时内存性能也较弱,可以开启ncnn内置的bf16加速开关,进一步降低神经网络推理的内存占用,...