First European RISC-V Summit Sees IBM, BSC “Future of Computing” Agreement

Using biological building blocks in place of traditional materials to assemble computers has been a research topic for many years, but recently the first potential commercial use cases have begun to emerge, centered on storage for large data sets. The DNA Storage Alliance, created to promote a storage ecosystem based on synthesized DNA strands, recently shared their aspirations for the emerging technology that offers significant promise in durability, simplicity, cost, and density over traditional magnetic counterparts. The initial goals of the alliance are to educate the public and raise awareness about DNA-based storage. Further out, the alliance may pursue the creation of specifications and standards, such as encoding, physical interfaces, retention, and file systems, to ensure that DNA-based solutions complement existing storage hierarchies. The alliance notes that expectations for the growth rate of current storage mechanisms cannot keep pace with the rising demand for data storage, particularly where growing data retention and related data mining efforts are driving the need to save increasingly larger data sets for longer periods of time. Such requirements are well suited to DNA-based archive storage characteristics in applications including digital content creation, robotics, smart cities, autonomous vehicles, healthcare, astronomy, and climate science.

At the conclusion of 2021, researchers at UNC Charlotte and Duke University Medical Center published results of work done to use transfer learning methods to generate fluence maps for radiation therapy, aimed at providing medical professionals with more capability and information in fighting adrenal cancers. The technique uses a deep transfer learning model trained on a much larger dataset that can be applied to a smaller data set for a specific application.

• The initial model was trained on pancreas treatment plans, then retuned and applied to a smaller set of data points on adrenal cancers. The output of the model generates a fluence map for specific IMRT beam-based treatments for adrenal cancers.
• According to the researchers, this approach is meant to supplement but not replace human expertise in the field and is reliant on human expertise to finetune and improve the AI model.