The post How Edge AI Can Enable Smarter, More Efficient Vehicles: Saharsh Singhania, Ambient Scientific appeared first at EVreporter on EVreporter.

Edge AI is increasingly being positioned as a foundational layer for the next generation of automotive features. To understand what this actually means for EVs, EVreporter spoke with Saharsh Singhania, Head of Product Marketing at Ambient Scientific, a company building AI chips based on an in-memory computing architecture. 

In this conversation, he explains why always-on features like parked vehicle guardian and battery monitoring need to run at the edge rather than the cloud, where Edge AI fits into a centralized-versus-distributed compute architecture, and why EVs — with their larger batteries — may be better positioned than ICE vehicles to adopt these systems at scale.

What makes Edge AI particularly relevant to the automotive and EV space today? 

What is Edge AI – Generally, when we say Edge AI, we mean that AI applications and computing for AI features run at the edge, not somewhere far away on a cloud server. Before we talk about how it benefits automotive and electric vehicles, let’s quickly recap the key benefits of running AI at the edge versus in the cloud.

With a traditional microcontroller or chip, the power consumption can be high enough to put the vehicle battery at risk of being drained. With a technology like ours, which enables accurate recognition through dedicated AI cores at very low power consumption, you can rely on the system being always on without significantly affecting the battery.

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