This project has received funding from the European Union's Horizon H2020 research and innovation programme under grant agreement No 956090
In the race towards building smarter, more powerful and more connected machines, scientists are also searching for ways to make sure they are energy efficient.
If no action is taken, computers will soon need more electricity than the world energy resources can generate. More alarmingly, the Internet of Things will connect up to 50 billion devices to the cloud through wireless networks.
The EU-funded APROPOS project is exploring ways to decrease energy consumption and optimise energy-accuracy trade-offs.
To that end, it is developing approximate computing, in which applications using data may be satisfied with an 'acceptable' level of accuracy, and it will train early-stage researchers in energy-accuracy trade-offs on circuit, architecture, software and system-level solutions.
According to the project, energy efficiency can thus be improved by a factor of up to 50 times.
The Approximate Computing for Power and Energy Optimisation ETN will train 15 ESRs to tackle the challenges of future embedded and high-performance computing energy efficiency by using disruptive methodologies.
Following the current trend, by 2040 computers will need more electricity than the world energy resources can generate. On the communications side, energy consumption in mobile broadband networks is comparable to datacenters.
To make things worse, Internet-of-Things will soon connect up to 50 billion devices through wireless networks to the cloud. APROPOS aims at decreasing energy consumption in both distributed computing and communications for cloud-based cyber-physical systems.
We propose adaptive Approximate Computing to optimize energy-accuracy trade-offs. Luckily, in many parts of the global data acquisition, transfer, computation, and storage systems there exists the possibility to trade off accuracy to less power and less time consumed.
As examples, numerous sensors are measuring noisy or inexact inputs; the algorithms processing the acquired signals can be stochastic; the applications using the data may be satisfied with an “acceptable” accuracy instead of exact and absolutely correct results; the system may be resilient against occasional errors; and a coarse classification may be enough for a data mining system.
By introducing a new dimension, accuracy, to the design optimization, the energy efficiency can even be improved by a factor of 10x-50x.
We will train the spearheads of the future generation to cope with the technologies, methodologies, and tools for successfully applying Approximate Computing to power and energy saving.
The training, in this first ever ITN addressing approximate computing, is to a large extent done by researching energy-accuracy trade-offs on circuit, architecture, software, and system-level solutions, bringing together world leading experts from European organizations to train the ESR fellows.
- TURUN YLIOPISTO
- KUNGLIGA TEKNISKA HOEGSKOLAN
- TECHNISCHE UNIVERSITEIT DELFT
- WIREPAS OY
- UNIVERSITEIT VAN AMSTERDAM
- TECHNISCHE UNIVERSITAET WIEN
Start date: 1 November 2020 - End date: 31 October 2024