This article is part ofTechXchange：AI在边缘
What you'll learn:
- The case for implementing AI in "small machines."
- What are the challenges to developing AI-enabled small machines?
在这一点上，我们应该有飞行汽车。和机器人管家。在运气不好的情况下，有情的机器人决定在我们造成启示录之前决定与我们相提并论。虽然我们没有那样，但显然人工智能（AI）技术已经进入了我们的世界。
Every time you ask Alexa to do something, machine-learning technology is figuring out what you said and trying to make the best determination on what you wanted it to do. Every time Netflix or Amazon recommend that “next movie” or “next purchase” to you, it’s based on sophisticated machine-learning algorithms that give you compelling recommendations that are far more enticing than sales promotions of the past.
And while we might not all have self-driving cars, we’re keenly aware of the developments in that space and the potential offered by autonomous navigation.
AI technology carries a great promise—the idea that machines can make decisions based on the world around them, processing information like a human might (or in a manner superior to what a human would do). But if you think about the examples above, the AI promise here is only being fulfilled by “big machines”—things that don’t have power, size, or cost constraints. Or to put it another way, they can get hot, have line power, are big, and are expensive. Alexa and Netflix rely on big, power-hungry servers in the cloud to figure out your intent.
While self-driving cars are likely to rely on batteries, their energy capacity is enormous, considering those batteries must turn the wheels and steer. They are big energy expenses compared to even the most expensive AI decisions.
因此，尽管人工智能的承诺很棒，但“小机器”被抛在后面。由较小的电池提供动力或具有成本和尺寸限制的设备无法参与机器可以看到和听到的想法。如今，这些小型机器只能利用简单的AI技术，也许会聆听单个关键字或分析低维信号（例如，从心率）等低维信号（PPG）。
What if Little Machines Could See and Hear?
But is there value in small machines being able to see and hear? It’s hard to think about things like a doorbell camera taking advantage of technologies like autonomous driving or natural language processing. Still, the opportunity exists for less complex, less processing-intensive AI computations such as vocabulary recognition, voice recognition, and image analysis:
- 门铃摄像机和消费者安全摄像机通常会引发无趣的事件，例如由风引起的植物运动，云造成的急剧变化，甚至是狗或猫在前面奔跑的事件。这可能会导致错误的触发因素，从而导致房主开始忽略事件。此外，如果房主在世界的另一个地区旅行，则他或她的相机可能会在他们的相机震惊时对日出，云层和日落引起的照明变化感到震惊。更智能的摄像头可能会触发更具体的事件，例如在参考框架中的人类。
- 门锁或其他接入点可以使用我的面部dentification 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.
这些示例只刮擦表面。允许小型机器看到，听到和解决以前需要人干预的问题是一种有力的想法，我们每天都会继续找到创造性的新用例。
使小机器能够看到和听到的挑战是什么？
因此，如果AI对小机器如此有价值，为什么我们还没有它呢？答案是计算马力。人工智能推断是计算神经网络模型的结果。将神经网络模型视为对大脑如何处理图片或声音的粗略近似，将其分解成很小的碎片，然后在将这些小块放在一起时识别图案。
The workhorse model of modern vision problems is the convolutional neural network (CNN). These kinds of models are excellent at image analysis and very useful in audio analysis, too. The challenge is that such models take millions or billions of mathematical computations. Traditionally, these applications have a difficult choice to make for implementation:
- 使用廉价且低功率的微控制器解决方案。尽管平均功耗可能很低，但CNN可能需要几秒钟才能计算，这意味着AI推断不是实时的，因此消耗了相当大的电池电量。
- 购买昂贵且高功率的处理器，该处理器可以在所需的延迟中完成这些数学操作。这些处理器通常很大，需要大量的外部组件，包括散热器或类似的冷却组件。但是，他们很快执行AI推断。
- 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.
需要的是从头开始构建的嵌入式AI解决方案，以最大程度地减少CNN计算的能源消耗。人工智能推论需要以比常规微控制器或处理器解决方案的能量少的数量级执行，并且没有外部组件（例如记忆）的帮助，这些记忆会消耗能量，大小和成本。
如果AI推导解决方案实际上可以消除机器视觉的能源惩罚，那么即使是最小的设备也可以看到并认识到周围世界上发生的事情。
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.One microcontroller, for example, is built to execute AI inferences while spending only microjoules of energy。
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