Efficient Signal Reconstruction via Distributed Least Square Optimization on a Systolic FPGA Architecture
Muya Chang, Samantak Gangopadhyay, Tomer Hamam, Justin Romberg, Arijit Raychowdhury
Abstract
Optimization problems form the basis of a wide gamut of computationally challenging tasks in signal processing, machine learning, resource planning and so on. Out of these, convex optimization, and in particular least square optimization, covers a vast majority; and recent advances in iterative algorithms to solve such problems of large dimensions have gained traction. Multi-core designs with systolic or semi-systolic architectures can be a key enabler for implementing discrete dynamical systems and realize massively scalable architectures to solve such optimization algorithms. In this paper, we present a platform architecture implemented in programmable FPGA hardware to solve a template problem in distributed optimization, namely signal reconstruction from non-uniform sampling. This is a quintessential problem with wide-spread applications in signal processing, computational imaging etc. We expect such an architectural exploration to open up promising opportunities to solve distributed optimizations that are becoming increasingly important in real-world applications. The complete system design, mapping and optimization into an FPGA architecture as well as analysis of convergence and scalability have been presented.
BibTeX
@inproceedings{icassp2019_efficientsignalr,
title = {Efficient Signal Reconstruction via Distributed Least Square Optimization on a Systolic FPGA Architecture},
author = {Muya Chang and Samantak Gangopadhyay and Tomer Hamam and Justin Romberg and Arijit Raychowdhury},
booktitle = {ICASSP 2019},
year = {2019}
}