How semiconductor innovations are making edge AI the future

It’s 6:42 a.m. and your alarm sounds. You feel well-rested, since the sleep-cycle monitor on your nightstand – enabled by built-in edge artificial intelligence (AI) and radar technology– helped you optimize your exact wakeup time by monitoring your heart rate and breathing. 

When you head down to the kitchen, your smart refrigerator helps you decide what to eat for lunch by scanning its contents using camera systems and making recommendations based on your dietary restriction data and preferences.

When it’s time to head to the office, you access and activate your car without touching anything. A camera outside recognizes you and automatically unlocks your car, which presets the temperature, seat adjustment and music volume to your preferences.

This future isn’t far off, and the technology that enables it is already here. Edge AI, the low-power microprocessors and easy-to-use software that help make it a reality rarely make the same headlines as buzzworthy generative AI and cloud-based computing – but they have the potential to shape how we interact with technology every day, and enhance our lives in ways we never considered.

What is edge AI?

When engineers refer to “the edge,” they’re not describing a far-away, abstract place. The edge exists in our homes, offices and factories. The edge is the local environment or device where the data is captured and computed, such as a robot or smart home device. AI at the edge is the ability to enable real-time intelligence and responsiveness on that device without sending data outside the local network to the cloud.

When compared to cloud-based AI resources, enabling this level of intelligence at the edge unlocks the potential of electronics to respond faster and more safely to the environment – impacting every industry, from automotive to medical applications and personal electronics. For example: cameras throughout a factory that are continuously inspecting equipment to detect and predict faults in machinery could halt the assembly process in a split second if needed.

“AI is about really fast pattern recognition that is reliable and secure,” said Artem Aginskiy, a product line manager. “And if you’re enabling AI at the edge, nothing has to leave the device, so it strongly supports privacy.”

Since edge AI is anticipating and responding to the local environment – without transmitting it to the cloud – there’s a reduced risk of data being compromised, such as health information in the sleep monitoring example.

Edge AI embedded processors

Until recently, artificial intelligence required specialized skills and experience that many organizations lack, making it seem out of reach. Still, as the transformative impact of AI becomes clear, companies big and small from almost every industry are seeking ways to integrate it into their products. Innovations in embedded processors and software are making edge AI more accessible, and its broad implementation closer.

Edge AI depends on embedded processors capable of running AI algorithms where the data is collected. But there are trade-offs in terms of energy efficiency and cost. Battery-powered systems like robotic vacuums and doorbells need cost-optimized processors that deliver optimized performance while minimizing energy use. 

High-performance systems such as factory automation, professional surveillance or robotics require higher processing performance, but cost and energy consumption are still factors.

Advances in chip design, like the integration of hardware accelerators low-power Arm® Cortex®-based microprocessors, are enabling the performance, speed and energy efficiency needed for edge AI at more affordable prices. By reducing system costs and power consumption in edge AI systems, designers can bring advanced AI capabilities to more applications, helping people fully leverage our electronics and the data they generate.

‘Low-code’ software development tools

Along with semiconductor innovations, new user-friendly edge AI software development tools are reducing the need for programming expertise when creating, training and deploying AI models. These tools help designers bring intelligence to their applications without needing in-depth coding expertise – a major obstacle in the past for edge AI implementation.

Tools like TI’s Edge AI Studio provide a “low-code” experience that allows designers to develop and test AI models without writing code, instead using GUI-based tools to extend the promise of artificial intelligence to non-experts. Designers can build neural networks, or AI algorithms – which serve as the intelligence and “brain” to the device – without specialized knowledge or skill.

“Texas Instruments is democratizing AI so any developer in the world can develop an intelligent system,” Artem said. “You can use the tool to train and deploy AI models without ever writing a line of code.” 

By making edge AI more accessible, electronics everywhere become smarter, safer, more productive and more reliable, impacting our lives in new ways we haven’t yet considered possible.