9July - October 2021by wind, drastic light changes caused by clouds, or even events such as dogs or cats running in front. This can result in false triggers, causing the homeowner to begin to ignore the events. In addition, if the homeowner is travelling in a different part of the world, they are probably sleeping while their camera is alarming to changes in lighting caused by sunrise, clouds, and sunset. A smarter camera could trigger on more specific events, such as a human being in the frame of reference. - Door locks or other access points can use facial identification or even speech recognition to grant access to authorized personnel, forgoing the need for keys or badges in some cases. - Lots of cameras want to trigger on certain events: for instance, trail cameras might want to trigger on the presence of a deer in the frame, security cameras might want to trigger on a person in the frame or a noise like a door opening or footsteps, and a personal camera might want to trigger with a spoken command. - Large vocabulary commands can be useful in many applications: while there are plenty of "Hey Alexa" solutions, if you start to think about a vocabulary of 20 or more words, you can find use in industrial equipment, home automation, cooking appliances, and plenty of other devices to simplify the human interaction. These examples only scratch the surface: the idea of allowing small machines to see, hear, and solve problems that in the past would require human intervention is a powerful one and we continue to find creative new use cases every day. What are the challenges to enabling little machines to see and hear? So, if AI could be so valuable to little machines, why don't we have it yet? The answer is computational horsepower. AI inferences are the result of the computation of a neural network model. Think of a neural network model as a rough approximation for how your brain would process a picture or a sound, breaking it into very small pieces and then recognizing the patter when those small pieces are put together. The workhorse model of modern vision problems is the convolutional neural network (CNN).These kinds of models are excellent at image analysis and are very useful in audio analysis as well. The challenge is that these models take millions or billions of mathematical computations. Traditionally, these applications have a difficult choice to make for implementation:- Use an inexpensive and low-powered microcontroller solution. While the average power consumption may be low, the CNN can take seconds to compute, meaning the AI inference is not real time, and meaning it consumes considerable battery power. - Buy an expensive and high-powered processor that can complete those mathematical operations in the required latency. These processors are typically large and require lots of external components including heat sinks or similar cooling components. However, they execute AI inferences very quickly. - Don't implement. The low power microcontroller solution will be too slow to be useful, and the high-powered processor approach will break cost, size, and power budgets. What is needed is an embedded AI solution built from the ground up to minimize the energy consumption of a convolutional neural network computation. AI inferences need to execute at orders of magnitude with less energy than conventional microcontroller or processor solutions, and without the assistance of external components such as memories, which consume energy, size, and cost. If an AI inferencing solution could practically eliminate the energy penalty of machine vision, then even the smallest devices could see and recognize things happening in the world around them. Lucky for us, we are at the beginning of this ­ a revolution of the "little machines". Products are now available to nearly eliminate the energy cost of AI inferences and enable battery powered machine vision. Find out more about a microcontroller built to execute AI inferences while spending only microjoules of energy. While the promise of AI is great, "little machines" are being left behind. Devices that are powered by smaller batteries or have cost and size constraints are unable to participate in the idea that machines can see and hear
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