AI solutions for edge platforms require unique optimization due to space, power, data security, and performance-latency constraints, necessitating tailored approaches for different applications like security cameras and robotics.
Edge AI entails transitioning from cloud-based training to on-device training, requiring new solutions to address the challenges of space, power, security, and performance-latency requirements.
While neural network models may start in data centers, edge applications require smaller models with high accuracy for real-time execution in critical sectors like healthcare, automotive, manufacturing, and security.
BrainChip's Akida neuromorphic IP offers a solution with Temporal Event-based Neural Networks (TENNs) to support event-driven AI in addition to traditional neural network architectures for edge computing applications.
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