Genio 1200-EVK

MT8395 System On Chip

Hardware

MT8395

CPU

4x CA78 2.2GHz, 4x CA55 2.0GHz

GPU

ARM G57

AI

APU 3.0 (2x MDLA 2.0, 2x VPU)

Please refer to the MT8395 (Genio 1200) to find detailed specifications.

APU

The MediaTek AI Processing Unit (APU) is a a high-performance hardware engine for deep-learning, optimized for bandwidth and power efficiency. The APU architecture consists of big, small, and tiny cores. This highly heterogeneous design is suited for a wide variety of modern smartphone tasks, such as AI-camera, AI-assistant, and OS or in-app enhancements.

The new APU 3.0 is scalable AI architecture, offering a huge 4 TOPS.

Overview

On Genio 1200-EVK, we provide different software solutions to boost AI computing by GPU and APU.

GPU Neural Network Acceleration

We provide tensorflow lite with hardware acceleration to develop and deploy a wide range of machine learning. By using TensorFlow Lite Delegates, you can enable hardware acceleration of TFLite models by leveraging on-device accelerators such as the GPU and Digital Signal Processor (DSP)

IoT Yocto already integrated the following two delegates:

  • GPU delegate: The GPU delegate uses Open GL ES compute shader on the device to inference TFLite model.

  • Arm NN delegate: Arm NN is a set of open-source software that enables machine learning workloads on Arm hardware devices. It provides a bridge between existing neural network frameworks and Cortex-A CPUs, Arm Mali GPUs.

APU Neural Network Acceleration

We introduce the MediaTek-proprietary Machine Learning solution: NeuroPilot on IoT Yocto on MT8395 P1V6 demo board (deprecated in v23.1).

NeuroPilot is a collection of software tools and APIs which are at the center of MediaTek’s AI ecosystem. With NeuroPilot, users can develop and deploy AI applications on edge devices with extremely high efficiency. This makes a wide variety of AI applications run faster, while also keeping data private.

On Genio 1200-EVK, we support Neuron SDK which is one of NeuroPilot software collections. Neuron SDK provides a Neuron compiler (ncc-tflite) to convert TFLite models to MediaTek-proprietary binaries (DLA, Deep Learning Archive) for deployment on MediaTek platforms. The resulting models are highly efficient, with reduced latency and a smaller memory footprint. Neuron SDK also provides Neuron Run-time API which provides a set of APIs that users can invoke from within a C/C++ program to create a run-time environment, parse compiled model file and perform on-device network inference.

../../../_images/sw_rity_ml-guide_g1200_sw_stack.png

Machine learning software stack on Genio 1200-EVK

Note

Software information, cmd operations, and test results presented in this chapter are based on the latest version of IoT Yocto (v23.0), Genio 1200-EVK.

Tensorflow Lite and Delegates

IoT Yocto integrated the Tensorflow Lite and Arm NN delegate to provide GPU neural network acceleration. The software versions are as follows:

Component

Version

Support Operations

TFLite

2.10.0

TFLite Ops

Arm NN

23.02

Arm NN TFLite Delegate Supported Operators

Note

According to Arm NN setup script, the Arm NN delegate unit tests are verified under TensorFlow Lite without XNNPACK support. In order to verify that the Arm NN delegate is properly integrated on IoT Yocto through its unit tests, IoT Yocto is configured not to enable XNNPACK support for TensorFlow Lite by default.

The following are the execution commands and results for the Arm NN delegate unit tests. All tests should pass. If XNNPACK is enabled in TensorFlow Lite, the Arm NN delegate unit tests will fail.

DelegateUnitTests
...
...
===============================================================================
[doctest] test cases:   670 |   670 passed | 0 failed | 0 skipped
[doctest] assertions: 53244 | 53244 passed | 0 failed |
[doctest] Status: SUCCESS!
Info: Shutdown time: 53.35 ms.

If you have to use Tensorflow Lite with XNNPACK, you can set the tflite_with_xnnpack as true in the following file: t/src/meta-nn/recipes-tensorflow/tensorflow-lite/tensorflow-lite_%.bbappend and rebuild Tensorflow Lite package.

CUSTOM_BAZEL_FLAGS += " --define tflite_with_xnnpack=true "

Supported Operations

Supported Operations

TFLite 2.10.0

Arm NN 23.02

abs

ABS

add

ADD

add_n

arg_max

ARGMAX

arg_min

ARGMIN

assign_variable

average_pool_2d

AVERAGE_POOL_2D

AVERAGE_POOL_3D

basic_lstm

batch_matmul

BATCH_MATMUL

batch_to_space_nd

BATCH_TO_SPACE_ND

bidirectional_sequence_lstm

broadcast_args

broadcast_to

bucketize

call_once

cast

CAST

ceil

complex_abs

concatenation

CONCATENATION

control_node

conv_2d

CONV_2D

conv_3d

CONV_3D

conv_3d_transpose

cos

cumsum

custom

custom_tf

densify

depth_to_space

DEPTH_TO_SPACE

depthwise_conv_2d

DEPTHWISE_CONV_2D

dequantize

DEQUANTIZE

div

DIV

dynamic_update_slice

elu

ELU

embedding_lookup

equal

EQUAL

exp

EXP

expand_dims

EXPAND_DIMS

external_const

fake_quant

fill

FILL

floor

FLOOR

floor_div

FLOOR_DIV

floor_mod

fully_connected

FULLY_CONNECTED

gather

GATHER

gather_nd

GATHER_ND

gelu

greater

GREATER

greater_equal

GREATER_EQUAL

hard_swish

HARD_SWISH

hashtable

hashtable_find

hashtable_import

hashtable_size

if

imag

l2_normalization

L2_NORMALIZATION

L2_POOL_2D

leaky_relu

less

LESS

less_equal

LESS_OR_EQUAL

local_response_normalization

LOCAL_RESPONSE_NORMALIZATION

log

LOG

log_softmax

LOG_SOFTMAX

logical_and

LOGICAL_AND

logical_not

LOGICAL_NOT

logical_or

LOGICAL_OR

logistic

LOGISTIC

lstm

LSTM

matrix_diag

matrix_set_diag

max_pool_2d

MAX_POOL_2D

MAX_POOL_3D

maximum

MAXIMUM

mean

MEAN

minimum

MINIMUM

mirror_pad

MIRROR_PAD

mul

MUL

multinomial

neg

NEG

no_value

non_max_suppression_v4

non_max_suppression_v5

not_equal

NOT_EQUAL

NumericVerify

one_hot

pack

PACK

pad

PAD

padv2

poly_call

pow

prelu

PRELU

pseudo_const

pseudo_qconst

pseudo_sparse_const

pseudo_sparse_qconst

quantize

QUANTIZE

random_standard_normal

random_uniform

range

rank

RANK

read_variable

real

reduce_all

reduce_any

reduce_max

REDUCE_MAX

reduce_min

REDUCE_MIN

reduce_prod

REDUCE_PROD

relu

RELU

relu6

RELU6

relu_n1_to_1

RELU_N1_TO_1

reshape

RESHAPE

resize_bilinear

RESIZE_BILINEAR

resize_nearest_neighbor

RESIZE_NEAREST_NEIGHBOR

reverse_sequence

reverse_v2

rfft2d

round

rsqrt

RSQRT

scatter_nd

segment_sum

select

select_v2

shape

SHAPE

sin

SIN

slice

softmax

SOFTMAX

space_to_batch_nd

SPACE_TO_BATCH_ND

space_to_depth

SPACE_TO_DEPTH

sparse_to_dense

split

SPLIT

split_v

SPLIT_V

sqrt

SQRT

square

squared_difference

squeeze

SQUEEZE

strided_slice

STRIDED_SLICE

sub

SUB

sum

SUM

svdf

tanh

TANH

tile

topk_v2

transpose

TRANSPOSE

transpose_conv

TRANSPOSE_CONV

unidirectional_sequence_lstm

UNIDIRECTIONAL_SEQUENCE_LSTM

unidirectional_sequence_rnn

unique

unpack

UNPACK

unsorted_segment_max

unsorted_segment_prod

unsorted_segment_sum

var_handle

where

while

yield

zeros_like

Demo

A python demo application for image recognition is built in the image that can be found in the /usr/share/label_image directory. It is adapted from the upstream label_image.py

cd /usr/share/label_image
ls -l

-rw-r--r-- 1 root root   940650 Mar  9  2018 grace_hopper.bmp
-rw-r--r-- 1 root root    61306 Mar  9  2018 grace_hopper.jpg
-rw-r--r-- 1 root root    10479 Mar  9  2018 imagenet_slim_labels.txt
-rw-r--r-- 1 root root 95746802 Mar  9  2018 inception_v3_2016_08_28_frozen.pb
-rw-r--r-- 1 root root     4388 Mar  9  2018 label_image.py
-rw-r--r-- 1 root root    10484 Mar  9  2018 labels_mobilenet_quant_v1_224.txt
-rw-r--r-- 1 root root  4276352 Mar  9  2018 mobilenet_v1_1.0_224_quant.tflite

Basic commands for running the demo with different delegates are as follows.

  • Execute on CPU

cd /usr/share/label_image
python3 label_image.py --label_file labels_mobilenet_quant_v1_224.txt --image grace_hopper.jpg --model_file mobilenet_v1_1.0_224_quant.tflite

  • Execute on GPU, with GPU delegate

cd /usr/share/label_image
python3 label_image.py --label_file labels_mobilenet_quant_v1_224.txt --image grace_hopper.jpg --model_file mobilenet_v1_1.0_224_quant.tflite -e /usr/lib/gpu_external_delegate.so

  • Execute on GPU, with Arm NN delegate

cd /usr/share/label_image
python3 label_image.py --label_file labels_mobilenet_quant_v1_224.txt --image grace_hopper.jpg --model_file mobilenet_v1_1.0_224_quant.tflite -e /usr/lib/libarmnnDelegate.so.28 -o "backends:GpuAcc,CpuAcc"

Benchmark Tool

benchmark_model is provided in Tenforflow Performance Measurement for performance evaluation.

Basic commands for running the benchmark tool with CPU and different delegates are as follows.

  • Execute on CPU (8 threads):

benchmark_model --graph=/usr/share/label_image/mobilenet_v1_1.0_224_quant.tflite --num_threads=8 --num_runs=10

  • Execute on GPU, with GPU delegate:

benchmark_model --graph=/usr/share/label_image/mobilenet_v1_1.0_224_quant.tflite --use_gpu=1 --allow_fp16=0 --gpu_precision_loss_allowed=0 --num_runs=10

  • Execute on GPU, with Arm NN delegate:

benchmark_model --graph=/usr/share/label_image/mobilenet_v1_1.0_224_quant.tflite --external_delegate_path=/usr/lib/libarmnnDelegate.so.28 --external_delegate_options="backends:GpuAcc,CpuAcc" --num_runs=10

Neuron SDK

On Genio 1200-EVK, IoT Yocto supports Neuron SDK which is one of MediaTek NeuroPilot software collections. Neuron SDK provides Neuron compiler (ncc-tflite) to convert TFLite models to MediaTek-proprietary binaries (DLA, Deep Learning Archive) for deployment on MediaTek platforms. Neuron SDK also provides Neuron Run-time API which provides a set of APIs that users can invoke from within a C/C++ program to create a run-time environment, parse compiled model file and perform on-device network inference. Please refer to Neuron SDK chapter to find all supporting detail.

Note

After IoT Yocto v23.0, you have to flash image with apusys device tree overlay (apusys.dtbo) to enable Neuron SDK support on Genio 1200-EVK.

genio-flash --load-dtbo apusys.dtbo

Supported Operations

Refer to Supported Operations to find all the neural network operations supported by Neuron SDK, and any restrictions placed on their use.

Note

Different compute devices may have restrictions on supported operations. These restrictions are a function of:

  1. Op Type

  2. Op parameters (e.g. kernel dimensions and modifiers, such as stride)

  3. Tensor dimensions (both input and output)

  4. Soc Platform

  5. Numeric format, both data type, and quantization method

Each device will have its guidelines and restrictions.

Demo

A python demo application for image recognition is built into the image that can be found in the /usr/share/demo_dla directory.

cd /usr/share/demo_dla
ls -l
-rw-r--r-- 1 root root   61306 Mar  9  2018 grace_hopper.jpg
-rw-r--r-- 1 root root   10479 Mar  9  2018 imagenet_slim_labels.txt
-rw-r--r-- 1 root root    1402 Mar  9  2018 label_image.py
-rw-r--r-- 1 root root 4276352 Mar  9  2018 mobilenet_v1_1.0_224_quant.tflite

Use cmd:python3 label_image.py to run the demo. The demo program will convert mobilenet_v1_1.0_224_quant.tflite into DLA, then inference it on APU to classify the image: grace_hopper.jpg. Finally, print out the result of image classification, it should be “military uniform”.

cd /usr/share/demo_dla
python3 label_image.py
/usr/share/demo_dla/mobilenet_v1_1.0_224_quant.dla
/usr/share/demo_dla/grace_hopper.bin
WARNING: dlopen failed: libcmdl_ndk.mtk.vndk.so and libcmdl_ndk.mtk.so not found
WARNING: CmdlLibManager cannot get dlopen handle.
[apusys][info]apusysSession: Session(0xaaaaf9d4eb80): thd(runtime_api_sam) version(3) log(0)
The required size of the input buffer is 150528
The required size of the output buffer is 1001
[apusys][info]run: Cmd v2(0xaaaaf9d737e0): run
[apusys][info]run: Cmd v2(0xaaaaf9d737e0): run done(0)
The top index is 653
The image: military uniform

Benchmark Tool

A python application for benchmarking is built in the image that can be found in the /usr/share/benchmark_dla directory.

cd  /usr/share/benchmark_dla
ls -l
-rw-r--r-- 1 root root 26539112 Mar  9  2018 ResNet50V2_224_1.0_quant.tflite
-rw-r--r-- 1 root root     9020 Mar  9  2018 benchmark.py
-rw-r--r-- 1 root root 23942928 Mar  9  2018 inception_v3_quant.tflite
-rw-r--r-- 1 root root  3577760 Mar  9  2018 mobilenet_v2_1.0_224_quant.tflite
-rw-r--r-- 1 root root  6885840 Mar  9  2018 ssd_mobilenet_v1_coco_quantized.tflite

Use cmd:python3 benchmark.py --auto to run the benchmark. It will find all TFLite models in /usr/share/benchmark_dla and compile them into DLA, then inference them on APU. Finally, the benchmark result will be saved in /usr/share/benchmark_dla/benchmark.log

cd  /usr/share/benchmark_dla
# run benchmark to evaluate inference time of each model in the current folder
python3 benchmark.py --auto

# check inference time of each model
cat benchmark.log
[INFO] mobilenet_v2_1.0_224_quant.tflite, mdla2.0, avg inference time: 3.2
[INFO] mobilenet_v2_1.0_224_quant.tflite, vpu, avg inference time: 14.1
[INFO] ssd_mobilenet_v1_coco_quantized.tflite, mdla2.0, avg inference time: 4.2
[INFO] ssd_mobilenet_v1_coco_quantized.tflite, vpu, avg inference time: 20.1
[INFO] ResNet50V2_224_1.0_quant.tflite, mdla2.0, avg inference time: 8.2
[INFO] ResNet50V2_224_1.0_quant.tflite, vpu, avg inference time: 56.3
[INFO] inception_v3_quant.tflite, mdla2.0, avg inference time: 11.2
[INFO] inception_v3_quant.tflite, vpu, avg inference time: 71.4

Benchmark Result

The following table are the benchmark results under performance mode

Average inference time(ms)

Run model (.tflite) 10 times

CPU (Thread:8)

GPU

ARMNN(GpuAcc)

ARMNN(CpuAcc)

APU(MDLA 2.0)

APU(VPU)

inception_v3

122.892

119.858

79.71

73.626

20.5

inception_v3_quant

34.783

119.36

48.013

29.062

9.05

74

mobilenet_v2_1.0.224

12.257

9.922

10.774

8.589

3.05

mobilenet_v2_1.0.224_quant

6.545

9.859

9.31

4.287

1.05

17

ResNet50V2_224_1.0

93.113

70.487

56.633

58.04

14.05

ResNet50V2_224_1.0_quant

39.116

72.569

28.887

22.733

6.05

59

ssd_mobilenet_v1_coco

30.674

30.06

25.131

28.373

7.05

ssd_mobilenet_v1_coco_quantized

10.173

32.344

17.313

9.026

2.76

23.57

Performance Mode

Force CPU, GPU, APU to run at maximum frequency.

  • CPU at maximum frequency

    Command to set performance mode for CPU governor.

    echo performance > /sys/devices/system/cpu/cpufreq/policy0/scaling_governor
echo performance > /sys/devices/system/cpu/cpufreq/policy4/scaling_governor

  • Disable CPU idle

    Command to disable CPU idle.

    for j in 2 1 0; do for i in 7 6 5 4 3 2 1 0 ; do echo 1 > /sys/devices/system/cpu/cpu$i/cpuidle/state$j/disable ; done ; done

  • GPU at maximum frequency

    Please refer to Adjust GPU Frequency to fix GPU to run at maximum frequency.

    Or you could just set performance mode for GPU governor, and make the GPU statically to the highest frequency.

    echo performance > /sys/devices/platform/soc/13000000.mali/devfreq/13000000.mali/governor

  • APU at maximum frequency

    Command to make APU at maximum frequency.

    echo dvfs_debug 0 > /sys/kernel/debug/apusys/power

    Or you can refer to QoS Tuning Flow to set qos.boostValue to NEURONRUNTIME_BOOSTVALUE_MAX.

  • Disable thermal

    echo disabled > /sys/class/thermal/thermal_zone0/mode