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.
We are investigating new stochastic theories and analytics including statistical learning techniques, machine training and inference systems and their applications to IoT, social media and big data platforms.
We are investigating technologies to maximize efficiency with in-memory data services, in-situ query processing on streamed data, and on-demand query engine customization using multi-objective compiler optimization.
We are investigating a three-pronged approach to holistic data platforms and datacenter efficiency: vertical integration to minimize data-movement; specialization to optimize work per service; and approximation to tailor work for output quality.
We are investigating technologies spanning from decentralized trust and cryptography to robustness and resiliency of natural processes to strengthen security, privacy and trust in data platforms and systems.