What Is the Future Outlook for Embedded Computing and Deep Learning for Robotics?
Jul 26, 2021
Many of the most exciting developments in AI are happening within edge applications that are powered by embedded computing. AI processing plays a vital role in many devices we use on a daily basis, including in things like smartphones and security cameras. More complex types of AI, like deep learning AIs, are also becoming increasingly important in advanced drone and robotic technologies.
AI in edge computing applications is vital for filtering and processing data locally at the source. Making decisions based on that data quickly with minimal latency is often important for various edge and robotic applications. This edge-side processing is also becoming increasingly important for complex edge devices and machines that might generate terabytes worth of data on a daily basis.
Applications of Edge AI
SMART Embedded Computing’s Health and Safety Screening system is an example of edge computing that leverages AI. The system’s advanced analytics can send alerts to supervisors when people in a specified area aren’t in compliance with mask mandates, have an elevated temperature or aren’t social distancing.
Of the millions of surveillance cameras operating in the world, only a small fraction are monitored at all times. Without AI, unmonitored surveillance cameras are essentially just a recording system for crimes rather than a tool that can be used to stop them.
Visual surveillance systems that leverage deep learning behavior analytics instead of the more traditional rule-based surveillance is becoming more prominent in security circles. In a rule-based system the AI is programmed to recognize certain actions or conditions and report them.
A non-rule behavioral analytics AI that leverages machine learning observes and learns what normal behavior entails (including from vehicles, machines and people) and can report abnormal behaviors in real time. When something breaks its determined pattern, the AI can report the event.
Systems that utilize behavior analytics and variations of machine learning require robust computational power, which is why powerful embedded computing is so important.
Deep Learning Versus Machine Learning
Deep learning is technically a type of machine learning. The basic purpose of machine learning is to enable machines to do more with less human input. Deep learning uses neural networks to get AIs to learn in a way similar to how a human brain might learn. The ultimate goal is still the same – get computers to do more with even less intervention from human programmers and analysts.
Even complex rule-based AIs can be demanding on hardware. The ability of embedded computing systems to handle AI depends largely on the complexity of the machine learning being employed. Deep learning tends to require more power than traditional machine learning.
Why Is the Development of Robotics Reliant on Embedded Systems?
Robots of all types are usually composed of subsystems. A centralized processing system may need to coordinate and control the actions of those subsystems. It’s technically possible for many robots to operate using external systems, but external control frequently results in a significant communication delay. Onboard embedded systems are generally vital for analyzing input from sensors and communicating to actuators to perform actions rapidly.
When combined with deep learning, advanced robotics can potentially automate a diverse array of tasks in an industry like manufacturing. They can be taught to excel at everything from assembly to quality assurance and packaging to handling.
Incorporating deep learning into future generation robots is likely going to be essential. It’s going to be necessary for robots to learn except in roles where the robot is assigned a very specific task in which they will never experience any deviation the programmer never expected. Powerful embedded computing systems will be necessary for these future independent deep learning robots to analyze and act on the chaos they might encounter while operating in unpredictable real-world environments.
SMART Embedded Computing develops a diverse array of embedded computing technologies, including edge computing, COTS for military applications, retail video analytics, health and safety screening systems, VoLTE and Vo5G, audio transcoding and much more.
We’re proud to develop embedded computing and edge solutions to drive innovation and growth in the industries in which we operate.
Contact us for more information on our embedded computing capabilities.