CV

Education

• Currently: Reliability Engineer (Intel)
• 2018-2021: PhD Degree, Computer Engineering (ASU, United States)
• 2017-2018: MS Degree, Computer Engineering (UFRGS, Brazil)
• 2013-2017: BS Degree, Computer Engineering (UFRGS, Brazil)

Work Experience

• Currently: Reliability Engineer @ Intel
  • Currently a member of Intel’s Advanced Reliability Characterization (ARC) group. I am responsible for designing and running a number of post-silicon tests, mostly aimed at (a) estimating vulnerability factors, (b) tracking fault propagation across architectural components, and (c) conceiving low-overhead mitigation strategies as new RAS features. Scripting is a key component in all of my work: from the control of physical equipment (i.e. motors, sensors, lasers), to the parsing and visualization of experimental data, automation and scalability are pervasive. Leveraging sets of hierarchically-organized scripts minimizes time-to-data, and allows me to dedicate a greater number of hours towards improving architectural reliability across generations of Xeon products.
• 2018-2021: PhD Student / Research Assistant @ ASU
  • I worked on novel hardening techniques as well as on dedicated RHDB accelerator architectures for matrix multiplication and neural networks on FPGAs/ASICs. Particularly, through extensive experimentation and analysis of multi-level fault models, my research developed error detection/correction methods with minimum added costs (compared to traditional modular redundancy). Moreover, I was involved in designing setups for beam experiments with FPGAs and with a fully custom chip (integrating multiple different compute units and test structures). Tangentially, I was also involved in a project on efficient SW/HW co-design, in which accurate kernel identification (SW) enabled optimal design decisions (HW), for increased performance.
• Summer 2019: Intern / Student @ LANL
  • The experience at LANL was very unique. I was fortunate to accumulate many hours of hands-on work with different types of radiation experiments (neutron generators for evaluating SEEs, gamma cells for evaluating TID effects, and laser sources for evaluating architectural vulnerabilities). I have learned a lot from a theoretical standpoint as well, since I have attended more than 50 hours of classes and lectures, from world-renowned researchers on particle physics, radiation testing, high-performance computing, space applications and beyond. As my summer project, I have evaluated the impact of reducing floating-point precision of processing elements in neural networks on FPGAs (both in terms of accuracy and radiation sensitivity).

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