(Special Topic Submission) Enabling Domain-Specific Architectures with an Open-Source Soft-Core GPGPU

Brandalero, Marcelo; Hernandez, Hector Gerardo Muñoz; Veleski, Mitko; Kadi, Muhammed Al; Rech, Paolo; Hübner, Michael

doi:10.1109/ipdpsw50202.2020.00015

Cited by 3 publications

(1 citation statement)

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“…Another interesting possibility is the use of soft-core GPUs on FPGAs, which can be optimized on-demand for different targets depending on the application requirements [18], [19]. Such an architecture has also shown promising results in the acceleration of fundamental kernels in ANN processing, like Matrix Multiplication, achieving an average 4.2× speedup for different workloads over an embedded ARM core with NEON support [20].…”

Section: Discussion: Embedded Process Controlmentioning

confidence: 99%

Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method

Fricke

Brandalero

Liehr

et al. 2022

IEEE Trans. Emerg. Topics Comput.

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Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.

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Section: Discussion: Embedded Process Controlmentioning

confidence: 99%