Province of Antwerp

Hyperdimensional Computing (HDC) is an emerging AI algorithm, touted to be an efficient, neuro-inspired and reliable alternative to neural networks for Edge AI. HDC utilizes hypervectors with several thousand elements; the number of elements in these hypervectors denotes the HDC dimension. This dimension can be optimized for improving the efficiency and reliability of HDC inference against errors such as bit-flips, which can be caused by environmental radiation-induced soft errors. We hypothesize that, by reducing the runtime chip area and execution time utilized by HDC inference through lowering dimensionality, both efficiency and reliability against soft error-induced bit-flips can be simultaneously improved while trading off a negligible amount of accuracy and error threshold. We tested our hypothesis by executing an HDC inference algorithm with two different dimension values, 10000 (10k) and 1024, on a commercially available, low-power, bare-metal ARM platform with a Cortex-M4 processor. We conducted the efficiency analysis by measuring the CPU cycles and energy required for executing the algorithm, and the reliability analysis using real-world atmospheric-like neutron radiation from the ChipIr facility in Oxfordshire, UK. Analyses revealed that, by lowering the HDC dimension from 10k to 1024, the reliability of HDC inference against soft error-induced bit-flips was 3.5 times better and efficiency improved by more than 16 times. This innovative observation contrasts the prevailing understanding in the community that increasing the HDC dimension always improves robustness or reliability. To the best of our knowledge, our work is the first to study the reliability of HDC inference using real-world radiation.

Demographic shifts and increased automation in chemical plants are reducing the experience and skill levels of plant operators. Therefore, BASF has implemented Operator Training Simulators (OTS) to allow operators to practice and improve their skills in this safe and controlled environment. The OTS consists of a dynamic model of the process, a control system and safety logics. This paper describes the learnings from using OTS at BASF, where they are used to train operators in process understanding, optimization, procedural training, and disturbance handling. Benefits include reduced training costs, minimized risks and improved efficiency. Also organizational guidelines are provided to ensure that the mentioned benefits are realized in industrial practice. Additionally, high-accuracy OTS models support HAZOP, debottlenecking, and optimization studies.

Ethiopic manuscript studies have become a rapidly expanding field in recent decades. However, most research has focused on cataloging and textual analysis. This study examines the material traces of original addenda, patterns of deterioration, and desecration of indigenous conservation ethics. A combination of codicological and paleographic methods was used. This approach is vital for documenting historical features, understanding the context of use, and informing conservation efforts. The research involved assessing twenty-eight physical manuscripts from two collections in Addis Ababa, Ethiopia. Additionally, twenty-seven digital copies from the Endangered Archives Program, the Hill Museum & Manuscript Library, and the University of Cambridge Digital Library repositories were consulted. The findings revealed original features like holes, repairs, and scribal corrections. Damage such as tears, creases, dirt, fading, erasures, and recent writing was also identified. These results reveal the material history of the manuscripts. Furthermore, both domestic and international stakeholders have adversely affected these manuscripts through erasure, dispossession, and appropriation. This study proposes ethical guidelines for recent additions to the manuscripts and for preserving the original addendum. It also underscores the necessity for additional material research, enhancements in conservation practices, and efforts to raise awareness.