What is Artificial Intelligence and Machine Learning for IoT Wireless?
One of the most highly anticipated developments in the IoT is the infusion of Artificial Intelligence and Machine Learning (AI/ML) in edge devices. By making IoT devices trainable, actionable, and capable of extracting information and learning from the environment, they become more contextually aware and ultimately more useful in a variety of ways. Silicon Labs’ wireless SoCs and MCUs support the use of AI/ML, enabling developers to add capability, reduce complexity and take advantage of low-power, low-cost, small-footprint design.
Artificial intelligence and machine learning can offer several benefits to developers of IoT edge nodes:
- Significant cost savings
- Reduced bandwidth use
- Faster design time
- Smaller designs & with Lower-power operation
- Greater data privacy and security
Optimized Bandwidth
Edge device sensors can generate vast quantities of raw data, and therefore occupy large amounts of bandwidth. AI end nodes can pre-process data help reduce bandwidth usage.
Faster Design Time
Specialized AI modeling software create models that are used by small application MCUs, thus avoiding complicated coding typically required to detect subtle differences in raw data.
Smaller Design & Low-Power
AI adds functional benefits and capabilities but without adding to the memory footprint or MCU requirements since code size tends to be reduced. Local processing, also reduces current as communications are reduced.
Data Privacy
With reduced datasets transfering to the cloud, bad actors have less data on which to engage in hacking activity. Smaller datasets enabled by AI/ML also help neutralize the ability for hackers to identify data patterns.
Based in San Jose, California, Edge Impulse is the leading development platform for embedded machine learning, free for developers, and used by over 1,000 enterprises worldwide.
tinyML Summit Companion Kit
Edge Impulse and Silicon Labs, together, along with numerous other industry-leading companies and are sponsoring the online virtual event “tinyML Summit 2021”. The annual event, held March 22-26, 2021 is focused on enabling ultra-low power machine learning at the edge. As part of the event, Edge Impulse is offering a formal hands-on training course for AI/ML using the Silicon Labs Thunderboard Sense 2 development kit. The kit comprises of a Cortex-M4 microcontroller, a wide variety of sensors, a microphone, Bluetooth Low Energy and a battery holder. You’ll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio - and even stream your machine learning results over BLE to a phone.
tinyML Summit attendees looking to take advantage of the hand-on course should:
Register for the TinyML conference
Register and sign up for the course: Tutorial #4 - Build Industrial-Grade tinyML applications with Edge Impulse
Order Thunderboard Sense 2 Kit*
Order ahead of the training event and allow for shipping time to benefit from the full hands-on experience.
Download Open Source Firmware
Download Edge Impulse firmware for the Thunderboard companion development board which is hosted on GitHub.
*Note: The first 250 registrants will receive the hardware (Thunderboard Kit from Silicon Labs) for free, compliments of Edge Impulse.
Thunderboard Sense 2 Advanced IoT Kit Details
The Thunderboard Sense 2 offers developers a compact feature-packed reference design. The kit was designed to provide a fast path to develop and prototype IoT products such as wireless sensor nodes. Being rich in a broad range of sensors makes it an ideal platform to illustrate the power of Artificial Intelligence in a small platform IoT device. Thunderboard Sense 2 is fully supported by the industry-leading Simplicity Studio tool suite and comes complete with a fully supported on-board J-Link debugger.
Wireless Gecko EFR32™
- Wireless SoC with multi-protocol radio
- ARM® Cortex® M4 core with 256 kB RAM and 1024 kB Flash
- Low Energy Consumption
- Flexible MCU peripheral interfaces
Kit Features
- 2.4 GHz radio configuration with on-board antenna
- Segger J-Link Integrated Debugger
- USB CDC virtual serial port
- 8 Mbit SPI ultra low power NOR flash for local storage
Mobile App
- Mobile app for Android and iOS
- iOS app implemented in swift
- Android app implanted in native code
Mobile App Features
- View sensor data
- Control LEDs and detect button pushes
- Stream sensor data to the cloud