We have investigated Machine Learning Interatomic Potentials in application to the properties of gold nanoparticles through the DeePMD package, using data generated with the ab-initio VASP program. Benchmarking was carried out on Au20 nanoclusters against ab-initio molecular dynamics simulations and show we can achieve similar accuracy with the machine learned potential at far reduced cost using LAMMPS. We have been able to reproduce structures and heat capacities of several isomeric forms. Comparison of our workflow with similar ML-IP studies is discussed and has identified areas for future improvement.

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In this paper, we propose a novel architecture synthesis method for SoC using VCores. VCores are reusable and configurable high-level descriptions. An initial SoC architecture, which consists of a CPU, buses, and peripherals, is generated based on an architecture template. The hardware and software tradeoff is possible on the architecture model after assignment of software VCores or hardware VCores. The assignment is based on the results of the architecture's performance estimation. We present a prototype of the synthesis for SoC architecture using VCores and an architecture level design experiment using this prototype.

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