data model

In a similar process of design where application needs are translated into system functions and IP blocks, there is a need to translate the application needs into the data model for the system. In a simple sensor system the data model might be the flow of data that represents the real world phenomena into some actionable consequence via a digital control function.  

There is a major effort to move Artiificial Intelligence ("AI") / Machine Learning ("ML") to the edge, that is deployed on devices in the real world as opposed to centralised in cloud based data centers. In addition to the primary input data there is an additional need to select an appropriate inference model to provide the function over the input data. There are many tool chains available and under development to undertake training of new AI/ML models.  These have there own development flows which will not be repeated here. (Please add links and any views of utility either as comments or updates to the machine learning interests section). 

This design flow takes as inputs the growing understanding of the application needs, either form initial design study or iterative learning, and produces the data models for the primary data and any additional data (inc. ML models) needed to process it. 

Understanding the data models will help determine key system parameters such as memory needs, communication bandwidths, etc.