Edge AI in Drones - Research Perspective
Edge AI is emerging as one of the crucial technologies in the current tech market owing to its combination of edge computing and artificial intelligence. There is no limit to potential use cases of Edge AI through multiple edge devices. Artificial intelligence algorithms harness the sufficient real-time data generated by these edge devices efficiently. Thus, multiple Edge AI solutions, as well as applications, vary from different products such as smartwatches, smart speakers, drones, robots, and many more. Let’s explore Edge AI solutions in drones.
Edge AI in Drones
The drone is one of the emerging and top Edge AI solutions in the current tech market. Edge AI allows drones to collect real-time data and reside in the same location leading drones to generate data requiring processing.
It enhances this edge device to be used in multiple industries, especially the construction sector across the world. Drones work on visual search, image recognition, object tracking, mapping and cartography, and many more. Edge AI helps drones with multiple objectives—real-time tracking, predictive maintenance, logo detection, and so on.
Applications of Drones with Edge AI
Edge AI allows efficient data analysis and output generation, based on the datasets gathered and transferred to the edge node by drones, and helps with the following objectives:
Real-time object recognition and tracking: Drones can track vehicles and traffic flow for security and safety reasons.
Predictive maintenance for aging infrastructure: Bridges, roads, and buildings deteriorate over time and may endanger the lives of millions. Drone-assisted monitoring can help ensure that needed repair works will be done in due course.
Face recognition: While this opportunity triggers debates over the ethics and legitimacy of using the technology, AI drones with facial recognition can be used for good in many cases.
Logo detection: Marketing teams can use drones to monitor the brand presence or collect data to estimate the real impact of brand logo placement.
Mapping and cartography services: Building maps might require appointing a specialist or an expert team to get into an area impossible to explore or dangerous to humans, whereas a drone-based solution fueled by edge AI will enable edge devices to work on their own.
How Edge AI Advancements Will Drive the Next Generation of Drone Innovation?
Several companies are already testing drones for several applications. It’s clear that there’s a huge demand for both remote-controlled and autonomous drones in the commercial sector, and that demand will only ramp up further as regulations for drones are sorted out. In the following, we will explore how the latest AI computing advancements are ushering in a new era of innovation for drones.
One of the critical challenges for drones is that they need extremely powerful processing that is also incredibly energy-efficient and in a small form factor. This is doubly true for drones that use high-definition or multiple cameras for computer vision (CV) applications. Today’s digital computing solutions are often power-hungry, impacting the flight times and battery life of drones. Furthermore, digital computing solutions have trouble running complex AI networks, which are critical to providing immediate and relevant information to the control station.
While digital solutions aren’t sufficient for the challenging requirements of many drone AI applications, a new computing approach can help eliminate these hurdles. Analog compute in-memory (CIM) can perform real-time AI processing – even with multiple large, complex deep neural networks (DNNs) – at a fraction of the power of a digital processing system. Analog CIM systems work by pairing analog computing with a non-volatile memory (NVM) like flash memory, unlike digital computing systems that rely on high-throughput DRAM. Whereas DRAM consumes a lot of power, analog CIM systems have significant power advantages by performing massively parallel vector-matrix multiplication and addition operations inside flash memory arrays.
Analog CIM systems also do not suffer from the latency of data propagating through digital logic gates and memory in the processor and written and readout of external DRAM, meaning that analog CIM systems can process compute-intensive AI workloads extremely quickly. Additionally, analog CIM systems are very compact, essential for drones with size and payload (i.e., weight) restrictions. Analog CIM systems can deliver big AI processing power in a small form factor thanks to the high flash density, making it possible to use a single flash transistor as a storage medium, multiplier, and adder (accumulator) circuit.
All of these factors make analog CIM systems ideal for a wide variety of AI video analytics applications for drones, including object detection, classification, segmentation, and depth estimation. These capabilities will open up new and exciting possibilities for drones in the coming years.
We look forward to seeing how powerful AI processing based on analog CIM technology will reshape the next generation of drones and open up new applications for almost every industry imaginable.