• 🌱 Currently, I am a Hardware Engineer at Semidynamics, working on modelling platforms and tools to enhance the RISC-V IP capabilities for the next-generation AI workloads (e.g., Neural Networks and LLMs) at Edge and HPC environments.Modelling platforms and tools to enhance the RISC-V IP capabilities for the next-generation AI workloads (e.g., Neural Networks and LLMs) at Edge and HPC environments.

• Formerly, I was assistant researcher in the High-Performance Computing Architectures and Systems (HPCAS) group through the SYCLOPS project at INESC-ID, Lisbon - Portugal. I also worked as an assistant professor in the Computing undergraduate courses at the University of Santa Cruz do Sul (UNISC). In addition, I was a Postdoctoral researcher in collaboration with the Microelectronics Group (GME) at the Federal University of Rio Grande do Sul (UFRGS).

• Academic background: I received a bachelor’s degree in computer engineering from UNISC in 2014, an M.Sc. degree in computer science (PPGC) from UFRGS in 2017, and a Ph.D. in Microelectronics (PGMICRO) from UFRGS in 2022. My thesis received the TTTC's E.J. McCluskey Best Doctoral Thesis award in 2024 (Latin America).

• Experience: for the past nine years, I've been researching and developing tools involving the implementation and evaluation of reliable embedded systems based on resource-constrained devices. My research activity focuses on modelling and simulation of robust MPSoCs, deep learning approaches targeting accelerated HPC and IoT Edge devices, and performance modelling of emerging RISC-V architectures.

• 👀 I’m also interested in developing tools to enable the deployment of Machine Learning models in resource-constrained devices, IoT edge, and aerospace environments while further exploring the soft error reliability of such an approach when considering the fault impact in both the system execution and the memory elements. Moreover, I am also interested in the modelling of virtual platform architectures, networks-on-chip, and multiprocessor platforms.

• I’m looking to collaborate on research and development of Machine Learning models, soft error reliability, resource-constrained and HPC RISC-V-based architectures, IoT systems, MPSoCs, parallel processing, and embedded systems.

Software/Code

Hardware/Virtual Platforms