Memory Issues For AI Edge Chips

Several companies are developing or ramping up AI chips for systems on the network edge, but vendors face a variety of challenges around process nodes and memory choices that can vary greatly from one application to the next. The network edge involves a class of products ranging from cars and drones to security cameras, smart speakers and even enterprise servers. All of these applications incorporate low-power chips running machine learning algorithms. While these chips have many of the same components as other digital chips, a key difference is that the bulk of the processing is done in or near the memory. With that in mind, the makers of AI edge chips are evaluating different types of memory for their next devices. Each comes with its own set of challenges. In addition, the chips themselves must incorporate low-power architectures, despite the fact that in many cases they are using mature processes rather than the most advanced nodes. AI chips -- sometimes called deep-learning accelerators or processors -- are optimized to handle various workloads in systems using machine learning. A subset of AI, machine learning utilizes a neural network to crunch data and identify patterns.