AI at the Edge

Chapter 4. Algorithms for Edge AI

There are two main categories of algorithms that are important in edge AI: feature engineering and artificial intelligence. Both types have numerous subcategories; in this chapter we’re going to explore a cross-section of them.

The goal is to provide an overview for each algorithm type from an engineering perspective, highlighting their typical usage, strengths, weaknesses, and suitability for deployment on edge hardware. This should give you a place to start when planning real-world projects, which we’ll walk through in the coming chapters.

Feature Engineering

In data science, feature engineering is the process of turning raw data into inputs usable by the statistical tools we use to describe and model situations and processes. Feature engineering involves using your domain expertise to understand which parts of the raw data contain the relevant information, then extracting that signal from the surrounding noise.

From an edge AI perspective, feature engineering is all about transforming raw sensor data into usable information. The better your feature engineering, the easier life is for the AI algorithms that are attempting to interpret it. When working with sensor data, feature engineering naturally makes use of digital signal processing algorithms. It can also involve chopping the data into manageable chunks.

Working with Data Streams

As we’ve seen, the majority of sensors produce time series data. The goal of an edge AI application is to ...