Under the initiative of the armasuisse – Cyber-Defence Campus, a team of EPFL scientists, including CYD Doctoral Fellow Dina Mahmoud of PARSA, recently presented the first proof-of-concept for undervolting-based fault injection from the programmable logic of a field programmable gate array (FPGA) to the software executing on a processing system […]

An exciting new development in the progress of Midgard, a novel re-envisioning of the virtual memory abstraction ubiquitous to computer systems, sees a tech leader funding research that will bring together experts from Yale, the University of Edinburgh and EcoCloud at EPFL. Global semiconductor manufacturer Intel is sponsoring an EcoCloud-led […]

Today’s data centres have an efficiency problem – much of their energy is used not to process data, but to keep the servers cool. A new server architecture under development by the EU-funded COMPUSAPIEN project could solve this. As the digital revolution continues to accelerate, so too does our demand […]

The Cloud Sustainability Days, organised by the EPFL EcoCloud Center, are beginning. LINK TO LIVE COVERAGE PROGRAM Please use the basement entrance: This conference is organised by the EPFL EcoCloud Center for sustainable cloud technologies, with the participation of the Swiss Datacenter Efficiency Association, the EPFL FUSTIC association, members of […]

Researchers at Ecocloud, the EPFL Center for Sustainable Cloud Computing, have pioneered an innovative approach to implementing virtual memory in data centers, which will greatly increase server efficiency. Virtual Memory has always been a pillar for memory isolation, protection and security in digital platforms. The use of virtual memory is […]

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.