For the purposes of computational storage, AI is taking what is traditionally seen in brain functionality and neurons, translating that into mathematical functions, and then creating specialized hardware, accelerators, and neural network engines that can process data.
Computational storage offers a better way to manage the types of data that AI applications use. When data characterization is needed, so are training models. The system can program the processor and then adjust the processor in real time as needed. Training and inference create the common high-level definition of what's required in any AI application. But the question remains, why would an application require AI in storage?
Today’s systems generate a lot of data at the edge. Applying AI techniques at the edge, instead of sending it back through the cloud, is becoming increasingly important due to the power, performance, and dollar costs of data movement. The value of computational storage is in reducing data movement, which is also important to optimizing AI applications. Computational storage in AI applications isolates the AI processing offline within the local storage, and then moves only the required data to the host or the data center.