Baparekraf Developer Day adalah kegiatan yang diadakan oleh Kementerian Pariwisata dan Ekonomi Kreatif/Badan Pariwisata dan Ekonomi Kreatif (Kemenparekraf/Baparekraf) dengan tujuan mengasah kemampuan teknis pengembang aplikasi di Indonesia. Kegiatan ini memungkinkan transfer pengetahuan dan standar industri secara langsung dari para praktisi yang telah sukses, khususnya pada bidang pengembangan aplikasi. Machine Learning Session Tema: Machine Learning pada Perangkat Portabel Speaker: Nurendrantoro (Technology Advisor - Perintis Teknologi Nusantara)

1. Machine Learning pada
Perangkat Portabel
Rendra Toro - Technology Advisor Perintis Teknologi Nusantara

2. Edge ML Explosion

3. ● Lower latency & close knit
interactions
Edge ML Explosion

4. ● Lower latency & close knit
interactions
● Network connectivity
Edge ML Explosion

5. ● Lower latency & close knit
interactions
● Network connectivity
● Privacy preserving
Edge ML Explosion

6. On device ML allows
for a new generation
of products

9. TensorFlow Lite is a framework for
deploying ML on mobile devices and
embedded systems
What is TensorFlow Lite?

10. Android/iOS
smartphone
Linux-based device
(e.g. Raspberry Pi, Coral
Dev Board)
Microcontroller
(e.g. Arduino)

11. Android/iOS
smartphone
Linux-based device
(e.g. Raspberry Pi, Coral
Dev Board)
Microcontroller
(e.g. Arduino)

12. Photos GBoard Cloud YouTube Assistant
Mercari Viber
1000s of Production Apps Use it Globally
Uber Airbnb
Hike

13. Classification
Prediction
Recognition
Text to Speech
Speech to Text
Object detection
Object location
OCR
Gesture recognition
Facial modelling
Segmentation
Clustering
Compression
Super resolution
Translation
Voice synthesis
Video generation
Text generation
Audio generation
Audio
Image
Speech
Text Content

14. https://www.tensorflow.org/lite/examples

15. Landmark
detection
Face
detection
Barcode
scanning
Image
labeling
Smart Reply
Language
Identification
Vision Custom
Natural Language
Model
Serving
Object
detection
and tracking
AutoML
Vision Edge
On-device
Translation
Text
recognition

16. Android/iOS
smartphone
Linux-based device
(e.g. Raspberry Pi, Coral
Dev Board)
Microcontroller
(e.g. Arduino)

19. Dev Board USB Accelerator System-on-Module PCI-E Accelerators Camera Module Enviro Module
A single-board
computer with a
removable
system-on-module
(SOM) featuring the
Edge TPU.
A USB accessory
featuring the Edge TPU
that brings ML
inferencing to existing
systems.
A fully integrated
System on Module in a
40mm x 48mm
pluggable module.
PCI-E device for easy
integration of Edge TPU
into existing systems.
A/E, B/M, M.2
5MP camera with ISP.
Connects to the Coral
Dev Board with ease
through the CSI camera
connector.
An accessory board that
provides temperature,
light, and humidity
sensing for IoT
applications.
Production Sensing
Prototyping
Launched in March 2019, now available in 36 countries through our distribution partners
Coral products

20. Android/iOS
smartphone
Linux-based device
(e.g. Raspberry Pi, Coral
Dev Board)
Microcontroller
(e.g. Arduino)

21. ● No operating system required - bare metal
● Tens of KB of RAM & Flash
● Only CPU, memory & I/O peripherals
● Ultra low power, always-on
● DSPs are specialized for audio and visual signal
processing
Small computer on
a single circuit
IO
RAM CPU ROM
MCU/DSP
What is a microcontroller? A DSP?

22. Over 1+ billion devices are already using
hotword detection

23. ● Official Arduino library -
run example code directly
from desktop and web IDEs
onto Arduino hardware
● Speech detection in 5
minutes - open source
models available to get
started quickly on Arduino
https://www.tensorflow.org/lite/microcontrollers
How to get started

24. Optimize for edge devices
Make models smaller and faster

25. ● Limited compute power
● Limited memory
● Battery consumption
● App size
Optimization is important

26. 1. Use mobile optimized model architecture
2. Apply quantization
3. Apply pruning
4. Leverage hardware accelerator
Methods

27. Inception v4 Mobilenet_V3_1.0_224
Top-1 Accuracy 80.0% 75.7% -6%
Top-5 Accuracy 95.0% 92.8% -3%
TF Lite Inference
Latency*
320ms 8.58ms
37x
faster
Model Size 170.7MB 22.0MB
7.8x
smaller
* Pixel 4 - 4 Threaded CPU, Oct 2020
Model Architecture

28. Source: Andrew Howard & Mark Sandler et.al., Searching for MobileNetV3 (2019)
Model Architecture

29. Reduce number of bits for model weights and activations
3e38
-3e38 0
-127 127
float32
min max
int8
Quantization

30. Model Float baseline
Post-training
quantization
Accuracy
drop
Mobilenet v1 71.03% 69.57% 1.46 p.p
Mobilenet v2 70.77% 70.2% 0.57 p.p
Resnet v1 76.3% 75.95% 0.05 p.p
Quantization

31. What does it mean?
● Drop connections during training.
● Dense tensors will now be sparse
(filled with zeros).
Pruning

32. Benefits
● Smaller models. Sparse tensors can be
compressed.
● Faster models. Less operations to execute.
Pruning

33. Result
● Negligible accuracy
loss at 50% sparsity
● Small accuracy loss
at 75%
Pruning

34. Mobilenet V1-1.0-224
CPU
37 ms
CPU 2.8x
13 ms
GPU 5.2x
7 ms
EdgeTPU
11.6x
3.2 ms
Quantized
Fixed-point
OpenCL
Float16
Quantized
Fixed-point
Floating
point
Hexagon DSP
13.2x
2.8 ms
Quantized
Fixed-point
Hardware accelerator
Pixel 4 - Single Threaded CPU, June 2020

35. Contact:
Rendra Toro
Technology Advisor
@rdatm
Learn more about TensorFlow Lite at
https://www.tensorflow.org/lite