The Power of TinyML: Bringing AI Inferencing to the Edge, Part — 1

TinyML has completely transformed how we approach artificial intelligence inferencing by enabling it to be performed at the edge of our devices. The potential of this cutting-edge technology is immense, with the power to revolutionize various sectors ranging from healthcare to agriculture and beyond.

Fig 1: Enabling applications to deploy the Machine Learning model onto Embedded Systems.

Background Story:

Artificial intelligence, or AI, has been a hot topic in the tech world for quite some time now, and it’s not hard to understand why. Artificial Intelligence (AI) is a game-changer that has the potential to transform our world. It can improve automation, facilitate predictive analytics, and enable intelligent decision-making. As an end-user, it’s important to note that while AI has incredible potential, its practical implementation requires significant hardware and computational resources. As a result, the majority of AI models are currently trained and operated on high-powered servers located in the cloud. Imagine the possibilities if we could integrate AI technology directly into our devices, without relying on a connection to the cloud.

This is where TinyML, or Tiny Machine Learning, comes in. TinyML is an emerging subfield of machine learning that is concerned with creating low-power, low-computing-requirement devices capable of inferencing locally and so eliminating the need for an Internet connection. When latency and connection are problems, as they often are in remote areas or with mobile devices, this technology shines.

What is TinyML?

TinyML is a field of machine learning that focuses on developing machine learning models that can run on edge devices with limited computing resources. It is an extension of traditional machine learning designed to operate on microcontrollers with limited memory, processing power, and energy.

TinyML models are small in size and require minimal computational resources. They are typically developed using techniques such as quantization, pruning, and compression, which reduce the size and complexity of the models without compromising their performance.

TinyML models are designed to be energy-efficient and can run on battery-powered devices for extended periods. As a result, they are ideal for applications that require real-time processing, such as voice recognition, gesture recognition, and image classification.

The Power of TinyML:

The development of TinyML has revolutionized the way we approach AI inferencing. By moving AI processing to the edge, it’s now possible to perform real-time data analysis without needing a cloud connection. This means that data can be analyzed in real-time, allowing immediate action and enabling predictive analytics for healthcare, agriculture, and manufacturing industries.

In healthcare, for example, TinyML has the potential to revolutionize the way doctors diagnose and treat patients. As a result, doctors can provide quicker, more accurate diagnoses and treatment plans by developing small, portable devices that can perform complex medical tasks, such as analyzing blood samples or monitoring vital signs.

In agriculture, TinyML can monitor crop health and soil conditions in real-time, enabling farmers to make smarter decisions about planting, fertilization, and irrigation. This technology can also monitor livestock health, reducing the risk of disease outbreaks and increasing overall herd health.

Applications of TinyML:

The applications of TinyML are vast and diverse. It is used in various fields, including healthcare, agriculture, transportation, manufacturing, and smart homes.

1. Healthcare: TinyML is used in healthcare for remote monitoring of patients and real-time detection of diseases. It is used in wearable devices to monitor vital signs like heart rate, blood pressure, and oxygen saturation. TinyML models are also used in medical imaging to detect abnormalities in X-rays, CT, and MRI scans.
2. Agriculture: TinyML is used in agriculture for crop monitoring and yield prediction. It is used in sensors to monitor soil moisture, temperature, and pH levels. TinyML models are also used in drones to monitor crops and detect pests and diseases.
3. Transportation: TinyML is used in transportation for real-time object detection and classification. It is used in autonomous vehicles to detect and avoid obstacles, such as pedestrians, other vehicles, and road signs. TinyML models are also used in traffic management to optimize traffic flow and reduce congestion.
4. Manufacturing: TinyML is used in manufacturing for predictive maintenance and quality control. It is used in sensors to monitor machine performance and detect faults before they occur. TinyML models are also used in defect detection to identify product defects and prevent them from being shipped to customers.
5. Smart Homes: TinyML is used for voice and gesture recognition in smart homes. It is used in virtual assistants, such as Amazon Alexa and Google Assistant, to recognize and respond to voice commands. TinyML models are also used in home automation to detect gestures and control devices like lights and thermostats.

Fig 2: The Arduino TinyML kit on the left and Nano 33 BLE: a set of onboard integrated sensors on the right side.

Several companies are already using TinyML to implement AI inferencing on edge devices. Here are some examples:

1. Google: Google has developed a platform called TensorFlow Lite for Microcontrollers, which enables developers to deploy TinyML models on microcontrollers. It is used in various Google products like Nest cameras, Google Home, and Pixel smartphones.
2. Arm: Arm, a semiconductor company, has developed a platform called Arm Cortex-M55 processor, which is designed to run TinyML models on edge devices. It is used in various applications, such as wearables, smart homes, and industrial IoT.
3. Qualcomm: Qualcomm, a semiconductor company, has developed a platform called Snapdragon, which enables TinyML inferencing on edge devices. It is used in various Qualcomm products, such as smartphones, smart speakers, and drones.
4. Arduino: Arduino, an open-source hardware and software company, has developed a platform called Arduino Edge AI, which enables developers to deploy TinyML models on microcontrollers. It is used in various Arduino products, such as boards, shields, and sensors.
5. Xnor.ai: Xnor.ai, an AI startup, has developed a platform called Xnor.built, which enables developers to deploy TinyML models on edge devices. It is used in various applications, such as smart cameras, drones, and robots.

These companies are leading the way in implementing TinyML and enabling AI inferencing on edge devices. They are making AI more accessible and affordable, empowering edge devices to make smarter decisions.

The Future of TinyML:

As the development of TinyML continues, we can expect to see more and more use cases for this technology. From smart homes to autonomous vehicles, TinyML has the potential to transform the way we interact with technology and the world around us.

According to a report by Allied Market Research, the global edge AI software market is expected to reach $1.94 billion by 2025, growing at a CAGR of 25.7% from 2018 to 2025.

One of the most exciting aspects of TinyML is its potential to be used in conjunction with other emerging technologies, such as 5G networks and the Internet of Things (IoT). By combining these technologies, we can create a world where devices can communicate with each other in real-time, enabling a new level of automation and predictive analytics.

Conclusion:

The development of TinyML is a significant step forward in the evolution of artificial intelligence. By bringing AI inferencing to the edge, we can perform real-time data analysis and enable new use cases in the healthcare and agriculture industries. As this technology continues to evolve, we can expect to see more and more use cases for TinyML, transforming how we interact with technology and the world around us.

References:

1. https://viso.ai/edge-ai/ai-hardware-accelerators-overview/
2. https://www.gocct.com/inference-at-the-edge/
3. https://steatite-embedded.co.uk/what-is-ai-inference-at-the-edge/
4. https://podcasts.apple.com/us/podcast/use-cases-of-ai-inference-at-the-edge-with-geoffrey/id670771965?i=1000497896683
5. https://github.com/tinyMLx/courseware/tree/master/edX
6. https://hdsr.mitpress.mit.edu/pub/0gbwdele/release/3

How does TinyML work? The internal workings!

Coming Up Next…………………………………………!

Reach out to me on LinkedIn https://www.linkedin.com/in/saijeevanpuchakayala/ if you want to know more about TinyML.