A study reports a new parallel-computing approach based on a combination of nanotechnology and biology that can solve combinatorial problems. The approach is scalable, error-tolerant, energy-efficient and can be implemented with existing technologies.
The pioneering achievement, published in Proceedings of the National Academy of Sciences,was developed by researchers from the Technische Universita?t Dresden and the Max Planck Institute of Molecular Cell Biology and Genetics, Dresden in collaboration with international partners from Canada, England, Sweden, the US, and the Netherlands.
Conventional electronic computers have led to remarkable technological advances in the past decades, but their sequential nature — they process only one computational task at a time — prevents them from solving problems of combinatorial nature, such as protein design and folding, and optimal network routing. This is because the number of calculations required to solve such problems grows exponentially with the size of the problem, rendering them intractable with sequential computing. Parallel computing approaches can, in principle, tackle such problems, but the approaches developed so far have suffered from drawbacks that have made up-scaling and practical implementation very difficult. The recently reported parallel computing approach aims to address these issues by combining well-established nanofabrication technology with molecular motors that are highly energy efficient and inherently work in parallel.
Importantly, the approach is fully scalable with existing technologies and uses orders of magnitude less energy than conventional computers, thus circumventing the heating issues that are currently limiting the performance of conventional computing.
Learn more: World’s First Parallel Computer Based on Biomolecular Motors
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