Simla Burcu Harma, a researcher on the ColTrain project, has received the Generation Google Scholarship for Women in Computer Science. @SimlaBurcu

DNN training and inference have similar basic operators but with fundamentally different requirements. The former is throughput bound and relies on high precision floating-point arithmetic for convergence while the latter is latency-bound and tolerant to low-precision arithmetic. Both workloads require high computational capabilities and can benefit from hardware accelerators. The disparity in resource requirements forces datacenter operators to choose between custom accelerators for training and inference or training accelerators for inference.

In their paper, the researchers introduce a nanowire-based device to create high-electron-mobility tri-gate transistors for power-conversion applications. Based on nanoscale structures, the novel transistor design significantly reduces heat loss during the energy conversion process.

Contrary to expectations, the experiment revealed that the respondents held on to their views firmly, regardless of the celebrity inputs or their esteem in the eyes of the respondents. It was also clear that respondents liked to hear an opinion identical to their own even if it came from a disliked celebrity. Conversely, a dissenting opinion by a celebrity or expert reduced the respondent’s empathy for that person.

Two EPFL students have developed PowerSGD, an algorithm that allows compression of the needed bandwidth without compromising the accuracy of the training.

CrowdNotifier, a new protocol developed in part at EPFL. It alerts people who attended an event where there was a risk of COVID-19 infection.

Researchers from EPFL and INRIA have developed the first online FL system called FLeet, which makes it possible to carry out machine learning on mobile devices in real-time without any impact on learning tasks.

The study, which has won the “Prize for the research and Personal Data Protection Emilio Aced” given by the Spanish data protection agency (AEPD), raises a potent question: Does the use of parental control apps justify the dangers arising from the collection and processing of private data?

An international group of scientists has collaborated to present a photonic hardware accelerator that is capable of operating at speeds of “trillions of multiply-accumulate operations per second,” far beyond the capabilities of existing computer processors.

As the applications of AI increase rapidly, the scientific community, as well as lawmakers, are concerned that unregulated AI can lead to misuse and abuse.
In this context, the European Commission took a clear stance by releasing a white paper in February 2020, which could lead to a regulatory framework for AI.