Sequential power per area optimization of multichannel neural recording interface based on dual quadratic programming

In this paper, we propose a novel method for power per area optimization under yield constrains in multichannel neural recording interface. Using a sequence of minimizations with iteratively-generated low-dimensional subspaces, our approach renders consistently improved power per area ratio and imposes no restrictions on the distribution of process parameters or how the data enters the constraints. The experimental results, obtained on neural recording interface circuits in CMOS 90nm technology, demonstrate power savings of up to 26% and area of up to 22% without yield penalty.