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The findings of the research project–including prototypes, benchmarks, and code—will be available as open source releases. That would enable the research community to build on the findings and further improve them over time. Conversely, the end users or developers can access the documentation, reports, and prototypes produced during the research to protect their code.

The research team plans to extend the precision of their weight generator circuit to support DNN applications that require weight precision higher than 4 bits. They are also exploring different types of digital-to-analog converter types for their weight generator circuit. Although the proposed circuit is applicable to any type of neural network, the EPFL researchers aim to benchmark their design with a recurrent neural network (RNN) workload and achieve a significant improvement in performance and energy-efficiency.

An eleven-member jury formed by Swiss business magazine Bilanz has announced the 100 most important heads of Switzerland who are at the forefront of digitization this year. The list of achievers has been sorted into various categories such as investors, blockchainers, scalers, transformers, administrators, drone acrobats, mentors, and data miners. Among the blockchainers is Professor Bryan Ford, who heads the Decentralized and Distributed Systems Lab (DEDIS) at EPFL’s School of Computer and Communication Sciences.