The demand for intelligent and efficient autonomous systems has never been greater. Industries seek to leverage on the potential of Internet of Things (IoT), Machine Learning (ML), and Edge Computing. However, traditional Machine Learning systems require substantial computational resources, thereby limiting their applicability in Edge Computing scenarios. To tackle this challenge, the arrival of TinyML has emerged, as a transformative field at the intersection of Machine Learning and Embedded Systems.
What is TinyML?
TinyML, short for Tiny Machine Learning, represents the convergence of two fields: Machine Learning and Embedded Systems. We are observing a transition from the centralized deployment of large Machine Learning models in the cloud to a more decentralized approach, emphasizing intelligence at the Edge. The domain of TinyML focuses on filling the gap of applying Machine Learning models to small resource constraint Embedded Systems, where running inference on the device is required.
There are several benefits of running the inference on embedded devices, such as,
- Real-time decision-making:
TinyML on Embedded Systems can make decisions on the spot without the latency associated with cloud-based processing. This is critical in systems where split-decision making is necessary, such as self-driving cars, autonomous robotics, and data-driven detection systems in industrial automation.
- Data Privacy and Bandwidth Reduction:
TinyML eliminates the need for constant internet connectivity. The data is stored and processed locally, which reduces the bandwidth related to data transfer, and concerns about data privacy related to security breaches of cloud-based systems.
- Efficiency and Cost Optimization:
TinyML empowers resource-constrained microcontrollers, reducing reliance on expensive hardware and lowering energy consumption.
Overcoming Challenges and Embracing Innovation
Although TinyML holds significant potential, the limitations associated with computational power remain a substantial constraint. However, investments in this subfield are growing to tackle the hardware related challenges. More microcontrollers are emerging to take on this problem with faster processors, increased memory, and hardware accelerators such as ARM’s “Ethos-U55 Machine Learning Processor”. Furthermore, on the aspect of the software side, Google’s TensorFlow Lite Micro library is dedicated to optimizing TinyML models and inferencing for limited RAM and processing power. It is just a matter of time before we see more applications that merge Machine Learning with Embedded Systems.
EmLogic’s Journey into TinyML
At EmLogic, our objective is to acquire the expertise in utilizing the appropriate tools and frameworks needed for creating efficient and intelligent “Edge devices”. Our primary focus is on the intricate art of deploying Machine Learning models on resource-constrained embedded devices, emphasizing the aspect of inference rather than the training of algorithms.
In our upcoming series of articles, we will delve deeper into the world of TinyML, exploring its key components, best practices, and real-world use cases.
About the author
As an Embedded Software Developer, Chrisander is driven by cutting-edge, intelligent Embedded Systems. He earned a bachelor’s degree in Electrical Engineering, and a master’s degree in Systems Engineering with Embedded Systems with a thesis on “Data-driven Detection and Identification of Undesirable Events in Subsea Oil Wells”. Through a combination of education and practical experience, he has established competencies in Systems Engineering, Machine Learning, Embedded Linux, Yocto Project, and Test Automation.