Most tools used for compliance and SBOM generation use SPDX identifiers
This change brings us a step closer to an easy SBOM generation.
Signed-off-by: Alin Jerpelea <alin.jerpelea@sony.com>
apps/mlearning/tflite-micro/tflite-micro/tensorflow/lite/micro/kernels/cmsis_nn/conv.cc:18:10:
fatal error: Include/arm_nnfunctions.h: No such file or directory
18 | #include "Include/arm_nnfunctions.h"
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~
Signed-off-by: chao an <anchao@lixiang.com>
-O3: reduce code size.
-DTF_LITE_STATIC_MEMORY: cause bugs on some cores.
+DTFLITE_EMULATE_FLOAT: robuster to emulate float cucalation by fix-point.
Signed-off-by: jihandong <jihandong@xiaomi.com>
The second argument of vgetq_lane_s32(__a, __b) needs to be initialized before compilation, so unroll the for loop. and correct the passed parameters.
Signed-off-by: xinhaiteng <xinhaiteng@xiaomi.com>
The complete implementation is placed separately in mLearning/tflite-micro/operators/neon, delete this part.
Signed-off-by: xinhaiteng <xinhaiteng@xiaomi.com>
Cortex-A compilation options are added to tflite-micro and cmsis-nn, and new operator compilation environments are configured.
Signed-off-by: xinhaiteng <xinhaiteng@xiaomi.com>
VELAPLATFO-25411
On the basis of CMSIS-NN, neon was used to optimize the Add operator, which calculates the offset and addition of eight input and output data in one loop.
Signed-off-by: xinhaiteng <xinhaiteng@xiaomi.com>
Based on CMSIS-NN, the Conv operator was optimized. Using Neon acceleration, multiply 8 input data and 8 filter data in a single loop; Using Im2col technology, convert the output data into a matrix, calculate 2 rows of input data and 4 rows of filter data in a single large loop, and obtain 2x4 output data.
Signed-off-by: xinhaiteng <xinhaiteng@xiaomi.com>
This option, which resolves to -w when CONFIG_CYGWIN_WINTOOL is
configured, is now appended to INCDIR in tools/Config.mk.
See git commit # 5eae32577e5d5226e5d3027c169eeb369f83f77d in the main
Darknet is an open source neural network framework written
in C and CUDA. It is fast, easy to install, and supports
CPU and GPU computation.
You Only Look Once (YOLO) is a state-of-the-art,
real-time object detection system
Signed-off-by: Alin Jerpelea <alin.jerpelea@sony.com>
only one .c needed for each function group
add -flax-vector-conversions to avoid build error on gcc && M55
Signed-off-by: Peter Bee <bijunda1@xiaomi.com>
only one .c needed for each function group
add -flax-vector-conversions to avoid build error on gcc && M55
Signed-off-by: Peter Bee <bijunda1@xiaomi.com>
NNABLA_RT should compile as a module to provide the necessary support
for the dnn test application
Signed-off-by: Alin Jerpelea <alin.jerpelea@sony.com>
- support float version of convolution
- support the CHW tensor layout
following function prototypes are added:
- arm_convolve_CHW_f32_basic_nonsquare()
- arm_convolve_CHW_q15_basic_nonsquare()
- arm_convolve_CHW_q7_basic_nonsquare()
- arm_nn_CHW_mat_mult_kernel_q7_q15()
NOTE:this patch will be contributed to SMSIS and reverted later from NuttX
Signed-off-by: Alin Jerpelea <alin.jerpelea@sony.com>
the CMSIS NN software library is a collection of efficient neural
network kernels developed to maximize the performance and minimize
the memory footprint of neural networks on Cortex-M processor cores.
Project https://github.com/ARM-software/CMSIS_5
The library is divided into a number of functions each covering
a specific category:
Convolution Functions
Activation Functions
Fully-connected Layer Functions
SVDF Layer Functions
Pooling Functions
Softmax Functions
Basic math Functions
The library has separate functions for operating on different weight
and activation data types including 8-bit integers (q7_t) and 16-bit
integers (q15_t). The descrition of the kernels are included in the
function description.
More information
https://www.keil.com/pack/doc/CMSIS/NN/html/index.html
Project license : Apache 2.0 License
https://github.com/ARM-software/CMSIS_5/blob/develop/LICENSE.txt
Signed-off-by: Alin Jerpelea <alin.jerpelea@sony.com>
