本文是TechXchange:AI在边缘
1. AI is science fiction.
True, AI began as a sci-fi fantasy popularized by visionary writers, but AI is here and now. There are many current applications, depending on how you define artificial intelligence. Although, after solving what was once a complex AI problem, it quickly seems obvious and therefore less “intelligent.” In the U.S., one of the first examples of AI being used at the edge concerned handwriting recognition of checks.
The smart home is full of AI at the edge with devices that learn behavior patterns: ovens that pre-heat when you leave work; thermostats that save money by not heating the home when no one is home; and lights that learn preferences based on different activities humans are engaged in within a room.
2. AI must be edge-device-driven or cloud-based.
Not so fast. Turns out there are a lot of cool hybrid implementations that combine the two approaches. Oftentimes, whether an AI implementation is either at the edge or in the cloud, it’s governed by considerations concerning bandwidth, processing costs, privacy, and regulation. Take, for example, the monitoring of front-door security monitoring. Streaming a 24/7 live feed from a camera to the cloud is wasteful and expensive when nothing is happening. But if significant activity is detected by edge-based AI, then cloud services can be activated to identify the caller or determine the action required.
3.边缘的AI必须很快。
毫无疑问，这有时是正确的。例如，如果使用AI来控制自动驾驶汽车，则必须超级快速。以55英里 /小时的速度，一秒钟的车辆将行驶80英尺以上，因此AI必须以数十毫秒的速度进行刷新。因此，基于云的系统的延迟是不可接受的。但是如上所述，有很多理由可以在边缘进行处理。在许多情况下，几秒钟的延迟已足够。系统速度要求是应用程序的条件，而不是AI实施的位置。
4. Humans will always beat the best AI.
就像我们的自我希望我们相信这一点一样，这不是真的。人类具有奇妙的适应性，快速和直观的学习者。但是在某些情况下，例如在扫描中识别肿瘤，基于AI的系统已被证明更可靠。自从深蓝色击败当时已经有20多年了World Chess Champion,Garry Kasparov. And very recently, AI researchers in Singapore have managed to teach industrial robots to do a task that’s beyond many of us—the ability to assemble pre-packed furniture.
5. AI是对我们隐私的威胁。
实际上，许多人通过避免需要与敏感数据和图像进行人类互动，将使用AI视为我们隐私的盾牌。Edge AI的拥护者感到兴奋，该技术可以避免将敏感音频和图像数据不必要地流到云中。随着时间的流逝，这也可能导致更容易遵守新的隐私法规，例如欧洲的新GDPR（通用数据保护法规）。
6.所有AI系统都需要高速硬件和云技术。
错误的。AI培训系统绝对需要使用最新的GPU或其他加速硬件非常快速，基于数据中心的处理。但是，出于带宽，成本，隐私和调节的原因，可以使用较低成本的硬件在边缘使用低成本硬件来部署AI推导系统。
7. AI at the edge is expensive.
当然，昂贵的是相对的，但是现在使用适合数百万美元生产量的FPGA芯片开发了实施AI的嵌入式视觉系统。在主要城市中，可以实施有意义的AI功能，仅占一杯咖啡的一半。
8. AI太耗力了，无法在边缘部署。
这种误解的原因有两个。首先，人们混合培训和推断。无法解决此问题，培训非常强，并且具有当前方法，并且具有功率要求，即使不是不可能在边缘实施，也很难实施。其次，许多AI的早期实现都使用了CPU和GPU的并行性有限，并且需要以高时钟速度（以及高功率）运行，以实现许多应用程序的可接受性能。
但是，像ASIC或FPGA中可能的大规模平行实现提供了适合边缘应用程序的功率水平。最近，通过低于1兆瓦的功耗，已经证明了诸如面部检测和钥匙词检测等功能的FPGA实施。
9.在边缘使用AI的系统是复杂的设计。
This was true five years ago, when researchers starting using convolutional neural networks (CNNs) for image processing. Implementing AI was not for the faint of heart. However, today, tools such as TensorFlow and Caffe make it easy to design and train networks—even more so as many researchers have released example networks that can be used as the starting point for a design. This has been complemented by a number of embedded hardware suppliers providing compilers that let developers implement networks on hardware suitable for edge applications. It’s now possible to go from concept to implementation within one or two weeks.
10.边缘计算可以是服务器。
It’s true—for many people, edge computing means an industrialized server sitting in their factory processing data. This definitely has many advantages relative to processing data in the cloud. However, moving processing to the sensor further reduces data traffic, and minimizes requirements for upstream servers while reducing latency.
11. Edge AI operates on high-resolution images.
Some new to the field assume that the AI algorithms require high-resolution images for good performance. However, this usually isn’t the case. Many of the latest AI algorithms use images that are 224 × 224 pixels or 448 × 448 pixels in size. And many practitioners have demonstrated useful capabilities at much lower resolutions. For example, one company recently demonstrated face-detection systems designed using 32- × 32-pixel images.
戈登的手是产品营销主管Lattice Semiconductor.
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