Tze-Meng Low, Qi Guo and Franz Franchetti (Proc. High Performance Extreme Computing (HPEC), 2015)
Optimizing Space Time Adaptive Processing Through Accelerating Memory-Bounded Operations
Preprint (317 KB)
Published paper (link to publisher)

Space-Time Adaptive Processing (STAP) is a technique for processing signals from multiple antenna elements over multiple time periods for target detection. As STAP algorithms are typical run on airborne platforms, they need to be both high performance and energy-efficient. Due to the high rate of processing required, many existing algorithms focus on reducing the dimensionality of the data, or exploiting structure in the underlying mathematical formulation in order to reduce the total number of floating-point operations (FLOPs), and consequently, the time for computation. While such algorithms target the FLOPs-intensive operations within the STAP algorithm, a significant portion of the compute time for most STAP algorithms is actually spent in low-FLOPs, memory-bounded operations. In this paper, we address the computation of these memory-bounded operations within the STAP algorithm using a 3D stacked Logic-in-Memory system. The imminent arrival of 3D stacked memory makes available high memory bandwidth, which opens up a new and orthogonal dimension for optimizing STAP algorithms. We show that more than 11x improvement in time, and 77x improvement in energy efficiency can be expected when a 3D stack is used together with memory-side accelerators to target the memory-bounded operations within STAP.

Adaptive processing, Acceleration, Memory, Optimizing