Efficient Low-Power Material Analysis using Neuromorphic Hardware: A spectral case study

摘要

In this paper, we propose an accurate and fast technique to perform qualitative spectral analysis. We train a bio-inspired dedicated ASIC with the spectral signatures of the constituents and test the network performance on real NMR, IR and artificially synthesised FT-IR binary and ternary mixtures. Constituents are detected with a best case accuracy of ~95%, ~79% and ~78% in order of their respective proportions. Ability to detect the presence of samples even when present in low proportions coupled with advantages gained in throughput and power enables it to be deployed for real time intelligent detection in portable hand-held spectrometers.