Real-Time AI At The Edge May Require A New Network Solution

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Real-Time AI At The Edge May Require A New Network Solution - Forbes

Quick Summary:

According to BrainChip, the numbers can be very significant, with demonstrations showing up to a 50x reduction in the number of model parameters, up to a 30x reduction in training time, and 5000x reduction in multiple-accumulate (MAC) operations with the same or better accuracy.

There is no one-size-fits-all for edge AI because of space, power, data security, and performance-latency requirements, which means there is not one solution for all AI applications.

TENNs ability to adapt to new input/events overcome some of the limitations of traditional neural networks while supporting future neural network models at a fraction of the die area and operating cost.