AI6 Lagos: Edge Computing Workshop
George Igwegbe is the Ambassador at Edge Impulse Network. He has experience in designing IoT hardware systems in metering (electricity), Computer Vision. He’s also worked in Hardware and Machine Learning.
He received his Bachelor’s degree in Mechanical Engineering from UNILAG. He is a certified Tensorflow Developer and co-organizer of TinyML Nigeria. Curator of “tinyml-papers-and-projects”.
George is interested in Machine Learning on Embedded Systems and Video Analytics.
George Igwegbe
ML Engineer / Co-organizer AI6 Lagos
Bio:
What you will learn by the end of the workshop
WEEK 1: INTRO AND SETTING UP
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What are Edge Devices: are pieces of equipment that serve to transmit data between the local network and the cloud. An edge device works by being near the source of the data it manages.
Wide Range of “Edge” Applications
Jetson Nano
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What are Edge Devices: are devices whose compute, memory, and energy resources are constrained and cannot be easily increased or decreased. These constraints may be due to form-factor considerations and cost.
Wide Range of “Edge” Applications
Jetson Nano
What is Edge Computing ?
Edge Devices possess both communication and computational capabilities.
Edge Computing - refer to computations being performed as close to data source as possible, instead of on far-off remote location. Edge computing is the practice of processing data physically closer to its source.
2020 -> 25 Billion devices
2025 -> $3.9 - $11 Trillion p.a
Overview of Artificial Intelligence
AI, in the broadest sense, describes the different ways a machine interacts with the world around it. To maximize our chance of achieving a given goal. At it core, ML is a simply way of achieving AI.
What is Machine Learning ?
Machine learning uses data and produces a program to perform a task
Introduction to AI
Machine Learning
Deep Learning
Edge AI
GPT3
GPT4
DALL.E
CLIP
PaLM 2
Deep Learning has a size problem?
*Shifting from state-of-the-art accuracy to state-of-the-art efficiency
175 Billion
170 Trillion
< 540 Billion
GPT3, GPT4, CLIP…..
*Shifting from state-of-the-art accuracy to state-of-the-art efficiency
Deep Learning has a size problem?
What is an Edge AI ?
Edge AI is the deployment of AI applications in devices throughout the physical world. It’s called “edge AI” because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center.
AI on the Cloud vs AI at the Edge
Why is ML Moving to Edge ? Benefit of Edge AI
BVAS?
Frictionless Access Control
Understanding the flow of traffic through intersections to better design roadways and reduce traffic congestion
Traffic Flow Management
Faster boarding for airplanes and trains and determining the occupancy of transportation carriages
Retail and Merchandising
Optimization
Understand customer behavior to provide richer experiences and optimize store layouts
Logistics and Cargo Companies
Keep better track of metrics such as how long it takes to load cargo onto an airplane and identify areas of inefficiency
Making parking management more efficient by minimizing the search for an open spot and more accurately determining the duration that a space is occupied
Examples of Edge Applications
Parking Lot Management
Examples of Edge Applications
Device: Arduino BLE
Flash memory: 1MB
Processor: 64MHz
Weight: 5.0g
Comm: I2C, SPI, UART..
Language: C/C++
Device: OpenMV H7+
Flash memory: 16MB
Processor: 480MHz
Weight: 17.0g
Comm: I2C, SPI, CAN, UART..
Language: C++/Micropython
Concepts in Edge AI
Device: Raspberry Pi
SDRAM: 1,2,4,8GB
Processor: 1.5GHz
Weight: 46.0g
Comm: I2C, SPI, CAN…
Language: Any
Device: Jetson Nano
SDRAM: 2,4GB
GPU: 128-core Maxwell
Processor: 1.43 GHz
Weight: 77.0g
Comm: I2C, SPI, CAN, UART.
Language: Any
Concepts in Edge AI
Device for the workshop
Jetson Nano: Device for the workshop
Nvidia Devices
Jetson Nano: Device for the workshop
Edge Impulse: Training model for the workshop
Resources and Expectation
Workshop Github Repository
Workshop Github - Link
Chapter 5: Tools and Expertise, page 136
What you will build by the end of the workshop
Image Classification
Image Regression
DLI Projects - Link
Deploying model on Mobile Phone/Laptop
Deploying model on Jetson Nano
Using edge platform
What you will build by the end of the workshop
Jetson Playlist - Link
Setting up Jetson Resource
Certificate at the end of the program…after 4 weeks & nurture Jetson Ambassadors in Nigeria..
SETTING UP JETSON
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Training Models: Setting up Edge Impulse Account
THANK YOU