On Mathematical Models of Optimal Video Memory Design

The big video data size today imposes huge pressure on storage. The variation and aging induced memory failures significantly influence the video output quality. Recently, researchers have developed different memory designs for videos, deep learning, and other data-intensive applications, which enables better energy-quality tradeoffs with design constraints. Unfortunately, designing memory has been proven to be a very challenging problem due to: 1) various design constraints; 2) multiple memory bitcell design options; and 3) challenging layout integration and cost analysis using different memory technologies. In this paper, we develop novel mathematical models for optimizing embedded video memory design without applying a time-consuming and laborious ASIC design process. The problems are formulated as nonlinear programs and integer linear programs. Different SRAM designs and hybrid SRAM and DRAM designs are considered in our models. The results of the numerical studies show that by applying our proposed method the average mean square error of the video storage can be greatly reduced, even by more than 90% in many cases.