Addressing the challenges of embedded analytics
Analytics are often touted as the solution to many problems across a variety of embedded applications such as surveillance, automotive, industrial, and even purpose-built high-performance compute servers. While there are a variety of processing solutions to run the many analytic algorithms that exist, it’s important that designers pick the technology that will be the most efficient and effective for their design. This is even more important in the area of embedded analytics where solutions are often extremely size and power constrained. In these embedded spaces especially, the real-time, math intensive architecture of digital signal processors (DSPs) are proving to be an extremely efficient processing solution.
Embedded analytics are all around us. They’re in our cars and our places of work and in our homes. Most new automobiles are great examples of intelligent analytics systems. Whether helping people to parallel park or automatically accelerating and braking as part of an adaptive cruise control system, advanced driver assistance systems (ADAS) are becoming increasingly commonplace.
Figure 1. Advanced driver assistance systems (ADAS) relies heavily on embedded analytics.
Another example of embedded analytics are security and surveillance cameras which have become increasingly advanced with their usage of analytics. They are now capable of running algorithms like trip zone detection, motion detection, people counting and tamper detection all within the camera itself. For even more advanced capabilities, these cameras can be connected to a networked video recorder (NVR) that can stitch images from multiple cameras together to present a more continuous view or run highly complex algorithms like facial recognition and object detection. Chances are that if you work in an office building, there are similar cameras protecting you. Less sophisticated networked cameras are now common for home monitoring and while the use of analytics is mostly done on a networked PC, it won’t be long before advanced features like identification capabilities enter this space.
Even if you don’t have a car or a camera running analytics, you most assuredly have products that were either inspected or shipped using embedded analytics systems. Machine vision systems are widely used to help assemble and inspect products ranging from smart phones to fruits and vegetables.
Data growth and the need for edge processing
It’s not newsworthy to say that the amount of data traffic is increasing. Cisco’s popular yearly report on networking traffic is always full of amazingly quotable trends like “This year the world will use more than half as much web data as was used in the entire history of the world prior to this year.” The predictions of the increase in data traffic in Cisco’s report are simultaneously staggering and perfectly believable. In addition to the continuing demand for higher definition video, there is the rapid increase in the sheer number of connected devices that are all sending data of some sort or another.
The key technology challenges for all the data being generated are to store it and to make it useful. Analytics are the key to being able to glean insights from all this data as well as shouldering the burden
Analytics are the key to discovering meaningful relationships and patterns buried in data and they can provide the ability to either facilitate making an intelligent decision or, in some cases, actually make a decision based on the data. Either way, by extracting useful information from the data, analytics provide a way to reduce network bandwidth by only transmitting the relevant information and not the entire data stream. Edge processing, where analytics are used near the sensors (edge) of the network to reduce the amount of data being transmitted, will be increasingly needed as storing all the data being generated will simply not be practical in our advancing connected world.
However, there are several constraints on these edge processing applications that need to be considered when choosing a processor. These systems require the processing element to be near the data collection source. This criteria often sets hard limits on the size and power of a processor. In a machine vision system, for example, the processor has to be placed within the camera enclosure. Many of these cameras need to be quite small so that they can be easily placed into an elaborate and expensive machine automation system. The small enclosure places requirements on the physical size of the processor as well as the lens and other electronics. Additionally the power consumed by the processor is important because imaging sensors are sensitive to heat. A processor that runs too hot will adversely affect the quality of the image.
Similarly, many systems using embedded analytics, like a lot of industrial systems, have a high requirement for reliability. These systems are required to run continuously for a long period of time without fail which requires a processor that can run in these rugged conditions without fail. Often times these systems will not allow a cooling fan to be used due to reliability concerns, further magnifying the importance of a low-power processor.