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MegEngine/dnn/src/arm_common
Megvii Engine Team 0380811218 feat(dnn/arm_common): add nchw44 8x8x16 stride1 stride2 5 years ago
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conv_bias feat(dnn/arm_common): add nchw44 8x8x16 stride1 stride2 5 years ago
convolution fix(dnn): midout at where neccessary in megdnn 5 years ago
cvt_color ci(copybara): fix copybara of arm 5 years ago
elemwise feat(dnn/arm_common/elemwise): elemwise ternary support chw44 5 years ago
elemwise_helper fix(dnn/arm): use vcvtq_f32_s32 for all arm code 5 years ago
elemwise_multi_type feat(dnn/arm_common/elemwise): elemwise ternary support chw44 5 years ago
local ci(copybara): fix copybara of arm 5 years ago
matrix_mul fix(dnn): midout at where neccessary in megdnn 5 years ago
pooling fix(mgb/fallback): fix conv1x1 and conv1x1_gemv nchw44 usable 5 years ago
reduce ci(copybara): fix copybara of arm 5 years ago
resize ci(copybara): fix copybara of arm 5 years ago
separable_conv ci(copybara): fix copybara of arm 5 years ago
separable_filter ci(copybara): fix copybara of arm 5 years ago
simd_macro feat(dnn): optimize f23 and f63 nchw44 winograd 5 years ago
type_cvt ci(copybara): fix copybara of arm 5 years ago
warp_affine ci(copybara): fix copybara of arm 5 years ago
warp_perspective feat(dnn/aarch64-arm_common): add mat_idx warppespective for aarch64/arm_common/naive 5 years ago
winograd_filter_preprocess perf(dnn/arm_common): add nchw44 winograd f73 5 years ago
elemwise_op.h feat(dnn/arm_common/elemwise): elemwise ternary support chw44 5 years ago
handle.cpp ci(copybara): fix copybara of arm 5 years ago
handle.h feat(dnn/arm): add armv7 nchw_nchw44 3x3s2 asm kernel 5 years ago
intrinsic_helper.h fix(dnn): fix can not inline small function with GCC compiler 5 years ago
neon_struct.h fix(dnn): fix can not inline small function with GCC compiler 5 years ago
quantized_converter.h ci(copybara): fix copybara of arm 5 years ago
utils.cpp ci(copybara): fix copybara of arm 5 years ago
utils.h feat(dnn/arm_common): add int8 nchw44 winograd f23_4x4 f23_8x8 compute float32/int16 output int8 5 years ago
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MegEngine 安装包中集成了使用 GPU 运行代码所需的 CUDA 环境，不用区分 CPU 和 GPU 版。 如果想要运行 GPU 程序，请确保机器本身配有 GPU 硬件设备并安装好驱动。 如果你想体验在云端 GPU 算力平台进行深度学习开发的感觉，欢迎访问 MegStudio 平台

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