Edge computing for space applications: Field programmable gate array-based implementation of multiscale probability distribution functions

Abstract

Data processing is a challenging problem in space applications. The limited bandwidth available for communication between satellites and the ground and the increasing resolution of scientific instruments make it virtually impossible to transfer all the data recorded on board. Although various mitigation strategies were developed, large amounts of on-board data are still lost. This paper presents a Field Programmable Gate Array (FPGA)-based architecture which is able to perform on-board nonlinear analysis of data and compute probability distribution functions of fluctuations. We propose two implementations for our solution, which can be used for space applications and also other computational contexts. On a spacecraft, the logic resources of the FPGA will typically be shared by several designs running various digital signal processing algorithms. That is why each algorithm should be designed in variants, optimized for different criteria, so that the entire group of algorithms makes an efficient usage of the FPGA resources. The proposed solution focuses on two major optimization criteria, area and speed, such that the FPGA resources are efficiently used. Also, the power consumption is at least two orders of magnitude less in comparison with classical software implementations. The solution was tested with both synthetic and real data and shows excellent results paving the way towards an application that can be ported on a space-grade FPGA.

FPGA Design for On‐Board Measurement of Intermittency From In‐Situ Satellite Data

“…Therefore, the quantitative estimator of intermittency adopted to be implemented in FPGA is the flatness. This is also a natural option as an FPGA solution is available for estimating the multi‐scale probability distribution functions (Deak et al., 2018). However, the step from computing PDFs to estimating their moments with FPGA devices is not straightforward, as will be described below.…”

“… FIFOs (LUTs configured as shift registers, SRL16/SRLC32) providing access at the data samples with a step of τ (from Deak et al., 2018). Flatness computation block.…”

“…Based on its versatility and opportunities for optimization of resources, we build a system relying on Field Programable Gate Arrays (FPGA). Several modules of this device, the On‐board Architecture for Nonlinear Analysis of data (OANA) are already released, like the IP cores modules devoted for the spectral and statistical analysis of fluctuations (Deak et al., 2018; Opincariu et al., 2019). Here, we discuss a new feature added to OANA, the FPGA technology designed to compute the moments of the probability distribution functions (PDFs) of fluctuations, namely the flatness parameter (see its definition in the next section).…”

Nanosystems, Edge Computing, and the Next Generation Computing Systems

“…Similarly, traffic lights augmented with hyperspectral imaging and chem-bio sensors can be made locally smart when combined with weather sensors and the unique local population and infrastructure signatures. More concrete EC applications include scalable framework for early fire detection [99], disaster management services [100], accelerometers for structural health monitoring [101], micro-seismic monitoring platform for hydraulic fracture [102], a framework for searchable personal health records [75,76,103], smart health monitoring [76,104] and healthcare framework [105], improved multimedia traffic [106], a field-programmable gate array (FPGA)-based system for cyber-physical systems [107] and for space applications [108], biomedical wearables for IoMT [73,76,109], air pollution monitoring systems [110], precision agriculture [111,112], diabetes [74] and ECG [109] devices, and marine sensor networks [113].…”

An FPGA-Based Solution for Computing a Local Stationarity Measure From Satellite Data

“…This study is part of a broader effort devoted to building a complex semi-autonomous digital signal processing library, able to apply on-board various digital signal processing techniques. Our library already includes modules dedicated to statistical [9] and Fourier [10] analysis. In [9] we described an FPGA-based solution to compute probability distribution functions of fluctuations.…”