This is a writeup on the feasibility of using TBSM for edge applications.
TBSM has three portions: first, a DLRM generates an input vector for each action in a time series. Second, every output of each DLRM in time series is basically used as an “embedding” for a second DLRM like model. While there are differences between the top and bottom section (especially with regards to normalization), both exclusively use dot products between “embeddings” and MLPs for computation.
This model is highly usable for edge applications when applied to models with relatively few data points per example, such as the Taobao dataset.