The Mont-Blanc 2020 project has a budget of 10.1 million euro, funded by the European Commission under the Horizon2020 programme. It was launched on 11th December at the Atos site in Les Clayes, France, with a kick-off meeting that gathered representatives of all partners.
The Mont-Blanc 2020 project intends to pave the way to the future low-power European processor for Exascale. To improve the economic sustainability of the processor generations that will result from the Mont-Blanc 2020 effort, the project includes the analysis of the requirements of other markets. The project's strategy based on modular packaging would make it possible to create a family of SoCs targeting different markets, such as "embedded HPC" for autonomous driving.
The project's actual objectives are to:
The project will have to tackle three key challenges to achieve the desired performance with the targeted power consumption:
1. understand the trade-offs between vector length, NoC bandwidth and memory bandwidth to maximize processing unit efficiency;
2. an innovative on-die interconnect that can deliver enough bandwidth to the processing units, with minimum energy consumption;
3. a high-bandwidth and low power memory solution with enough capacity and bandwidth for Exascale applications.
"The ambition of the consortium is to quickly industrialize our research. This is why we decided to rely on the Arm instruction set architecture (ISA), which is backed by a strong software ecosystem. By leveraging the current efforts, including the Mont-Blanc ecosystem and other international projects, we will benefit from the system software and applications required for successful usage", explained Said Derradji, Atos, coordinator of the Mont-Blanc 2020 project.
Mont-Blanc 2020's goal is to initiate a family of processors that will be the basis for European Big Data / High Performance Computing exascale systems, and that will achieve market adoption and economic sustainability.
The Mont-Blanc 2020 project is run by a European consortium that includes:
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 779877.