Complete for freeIntermediateBrowser simulator included

Course 18

Hardware Lab: TinyML and Edge AI

Evaluate and design TinyML systems with sensor windows, classification, quantization, constrained memory, latency, deployment, accuracy, power, and local-inference privacy. The course combines structured lessons, guided browser concept simulations, and learner-created project files made with freely installable or open tools described in the course instructions. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure.

Free completion promise: The always-free pathway combines edge-budget concept simulations with learner-created deployment and evaluation files. The C4T page does not supply or run models, and modeled values are not physical measurements; boards, GPUs, and cloud services are optional.

Before you begin

Requirements & setup for Hardware Lab: TinyML and Edge AI

Start with the complete free pathway below. Hardware, subscriptions, paid APIs, and cloud credits are never required.

Start here · free path

Your complete $0 setup

The always-free pathway combines edge-budget concept simulations with learner-created deployment and evaluation files. The C4T page does not supply or run models, and modeled values are not physical measurements; boards, GPUs, and cloud services are optional.

$0 required

Setup checklist

0 of 9 complete

This checklist is a temporary setup aid and resets when the page reloads. Saved course progress begins in the setup lessons below.

1. Choose the free learning path
Use the browser-based tools and simulations throughout this course. Paid software, APIs, cloud credits, GPUs, and physical equipment are optional.
About 2 min
2. Set up C4T TinyML Edge Concept Simulator
Open the concept simulator and read its scenario, assumptions, and limitations.
About 5 minOpen C4T TinyML Edge Concept Simulator (opens in a new tab)
3. C4T TinyML Edge Concept Simulator: step 2
Make a prediction, adjust the available controls, and compare the result with the explanation.
About 5 min
4. C4T TinyML Edge Concept Simulator: step 3
Record what changed and why; repeat with at least one different input.
About 5 min
5. C4T TinyML Edge Concept Simulator: step 4
For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.
About 5 min
6. Set up TinyML Edge Concept Simulator
Open C4T TinyML Edge Concept Simulator.
About 5 minOpen TinyML Edge Concept Simulator (opens in a new tab)
7. TinyML-capable development board: step 2
Select the simulated tinyml-capable development board pathway.
About 5 min
8. TinyML-capable development board: step 3
Complete this check: Complete the board-specific safety and measurement checklist before optional deployment.
About 5 min
9. Run the setup check
C4T TinyML Edge Concept Simulator: Complete one prediction-and-explanation cycle and record the evidence you would carry into a learner-created project file. TinyML-capable development board: Complete the board-specific safety and measurement checklist before optional deployment.
About 5 min

Software

1 item

C4T TinyML Edge Concept Simulator

Course need

C4T TinyML Edge Concept Simulator

Free

A free browser concept simulator with adjustable values, a prediction prompt, deterministic feedback, and a short explanatory trace. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure. Use keyboard controls and the text trace or written alternative when a visual or pointer interaction is not suitable.

Open the concept simulator and read its scenario, assumptions, and limitations.
Make a prediction, adjust the available controls, and compare the result with the explanation.
Record what changed and why; repeat with at least one different input.
For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.

Open C4T TinyML Edge Concept Simulator

No-hardware path

TinyML Edge Concept Simulator

Explore course concepts through adjustable values, prediction, deterministic feedback, and an explanatory trace. No hardware is required.

Hardware

1 item

TinyML-capable development board

Optional board and sensor for repeating deployment and measurements physically.

Optional

TinyML Edge Concept Simulator

Free

Explore course concepts through adjustable values, prediction, deterministic feedback, and an explanatory trace. The physical equipment is optional. The simulator, supplied traces, and written tables cover the assessed concepts without handling physical parts.

Open C4T TinyML Edge Concept Simulator.
Select the simulated tinyml-capable development board pathway.
Complete this check: Complete the board-specific safety and measurement checklist before optional deployment.

Open TinyML Edge Concept Simulator

No-hardware path

TinyML Edge Concept Simulator

Explore course concepts through adjustable values, prediction, deterministic feedback, and an explanatory trace. No hardware is required.

Optional paid alternatives

You do not need these options to complete the course.

Edge AI development board

Optional physical extension; the always-free assessment uses guided concept results and learner-created evaluation files.

Optional only—never required for course completion.

Accounts

Nothing required

No external account is needed for this course.

What you will be able to do

Learning outcomes

Choose between cloud and edge inference from system constraints.
Prepare sensor windows for time-series, image, or audio classification.
Measure quantization, memory, latency, accuracy, and power trade-offs.
Document a private and testable constrained-device deployment plan.

Included downloads

Free starter assets

Original · synthetic · licensed

These small files contain no personal data, credentials, or secrets. Dataset fixtures are CC0 and starter code is MIT licensed.

Starter pack guide & licensesSee what each original synthetic dataset, fixture, and starter file contains.Simulated hardware sensor traceSynthetic device readings for no-hardware analysis and replay.ML evaluation fixtureFixed, synthetic classification, retrieval, research, and deployment values.

Build real work

Assignments & capstone

Free tools accepted

Quantization and device-budget study

Compare full and quantized models against accuracy, flash, memory, latency, and power limits.

Edge deployment and privacy plan

Design a deployment checklist, test protocol, duty cycle, and local-data policy for a learner-selected device scenario.

Final assessment

Constrained edge-AI device plan

Design a gesture detector, wildlife monitor, or smart safety device using a learner-created or lawfully obtained model, deployment plan, simulated or physical accuracy, latency, memory, power, and privacy analysis. Deliverables: Sensor and model selection, Quantization and device-budget evidence, Deployment and evaluation procedure, Accuracy, latency, memory, and power results, and Privacy and limitation analysis.

Prerequisites & pacing

Skills assumed

ML evaluation basics, Python, and IoT data flow concepts

Recommended prior courses

These are advisory, not locks. If you already have the skills listed above, enroll directly.

Choose your pace

self paced: 10 weeks at 5 hours per week
weekly: 10 weeks at 5 hours per week

Part of this learning path

🤖 Hardware and Robotics · step 4 of 4
Go from first LED to a connected, sensing, machine-learning device — every step has a browser simulator.

Course created by Zafir Shamsi.