Internet of things (IoT) has barged into our daily lives tremendously in recent times. It has already changed huge chunks of the technology-sector and is continuing to do so, with seemingly boundaryless, untapped opportunities. It has rapidly changed our lives on an exponentially large scale. Embedded ML and IoT are the new trending advancements in the world of technology.
However, IoT faces two main challenges: battery life for off-grid IoT applications and the capability of edge devices to connect over a long-range. Raw data transmission still is an area of high power consumption for any device. There is an ever-growing requirement for low-power edge computing devices that can minimize the transmission payload and integrate LPWAN (Low-Power Wide-Area Network) technologies that offer wide range connectivity with the bonus of long battery life. One of the most promising LPWAN technologies today is LoRa (long-range).
By implementing embedded machine learning with IoT applications or LoRa, transmitted data could be compressed by 512 times thereby extending the battery life by three times. However, the COVID-19 has inadvertently disrupted the global supply chain which is thus expected to hamper innovation and growth of embedded machine learning. Various new possibilities of embedded machine learning on IoT devices are expected to rise in 2021 as industry watchers predict. Some of the areas under consideration are:
Forced Interprice Maturation Issue
It might not be feasible to run full-scale deep learning models that frequently have billions of mathematical operations on smaller devices with a considerably limited amount of memory.
Acceleration of ML and AI
The Artificial Intelligence (AI) chips market, though impacted by the COVID-19 pandemic, is expected to grow at a CAGR of over 42% between 2020 and 2024. Increasing the adoption of AI chips in data centers will surely accelerate its growth. Infused by ML, digital insurance platforms now research applicants drive records, scrutinize data, etc. without face-to-face communication.
Embedded ML in Manufacturing Industry
Several industries have embraced many AI/ML conversations in 2020 as companies looked to leverage deep learning and neural networks to predict time-series data. Manufacturers today face challenges with unpredictable and complex machinery failures. With the availability of, massive time-series data on machinery examination and inspection, Embedded ML and machines identify certain areas that need to be efficiently tested.
Cloud and IoT sensors play an important role in modernizing the manufacturing industry. Embedding them in machinery, manufacturers can better predict the maintenance and equipment failure issues that might occur in the future. The embedded ML solutions automate the manufacturing process entirely along with smart manufacturing operations.
Cloud and IoT sensors play an important role in modernizing the manufacturing industry. Embedding them in machinery, manufacturers can better predict the maintenance and equipment failure issues that might occur in the future. The embedded ML solutions automate the manufacturing process entirely along with smart manufacturing operations.
COVID-19 pandemic has undoubtedly accelerated the development of ML applications solving today’s problems in the field of research, digital workforce, etc.
Follow and connect with us on Facebook, Linkedin & Twitter
