AI Solutions will enable machines to improve their own performance and make insightful decisions needed for autonomous manufacturing moving towards Industry 4.0.
A trend in many industries is to transition from simple edge devices, feeding data to the cloud for analysis, to perform sophisticated inferencing and pattern-matching at the edge. Edge AI can respond faster than cloud AI by eliminating the need to send large data volumes to the cloud.
Data security is enhanced, because less data is vulnerable to tampering as it is sent across networks. For mobile applications, Edge AI reduces the reliance on unreliable network connections (i.e., dead zones and service outages) by performing AI functions locally.
Some AI computing solutions are heterogeneous, as they use graphics processing units (GPUs) to speed up parallel task processing, use CPUs to manage large amounts of data and runs statistical computations. Compared to homogeneous systems (with either CPU or CPU), heterogeneous systems typically have better system responsiveness, but they do present some significant design challenges:
- Power consumption: GPU + CPU consumes more power than GPU or CPU
- Form factor: GPU + CPU requires more space than GPU or CPU
- Product availability: Many GPUs have relatively short life spans