An Exploration of FPGA based Multilayer Perceptron using Residue Number System for Space Applications
Avi Jain, Eswar Deep Pitchika, Shivang Bharadwaj · 2018
In recent times, most satellite applications consist of complex and computationally intensive data processing systems. The challenge is to meet the demands of onboard processing while keeping the power consumption at a minimum. In this paper, we explore the scope of an FPGA based neural network using Residue Number System for space-based applications. We propose an implementation that uses RNS arithmetic to exploit the parallelism present in neural networks for faster computations thus meeting onboard processing demands of satellites without the use of high powered CPUs or GPUs.