The LEGaTO project received EU funding to develop an integrated software stack to improve energy efficiency for heterogeneous hardware by 10x. The stack will support task-based programming and be tested on commercial CPU-GPU-FPGA hardware. It aims to decrease failure rates by 5x through software fault tolerance and reduce the trusted computing base size by 10x. Use cases for healthcare, machine learning and smart homes/cities will demonstrate energy savings and improved resilience. Chalmers is enhancing the XiTAO runtime as part of LEGaTO to reduce overheads and parallel slackness for up to 90% energy savings.

1. The LEGaTO project has received funding from the European Union’s Horizon 2020 research and innovation programme
under the grant agreement No 780681. www.legato-project.eu
The LEGaTO Software Toolchain
Low Energy Toolset for Heterogeneous Computing
Miquel Pericàs (miquelp@chalmers.se)
Mustafa Abduljabbar (musabdu@chalmers.se)
Department of Computer Science and Engineering, Chalmers University of Technology
Project Goals
One order of
magnitude improvement
in energy-efficiency
for heterogeneous
hardware through the
use of energy
optimized-programming
model and runtime.
Starting with Made-in-
Europe mature software
stack, and optimizing this
stack to support energy-
efficiency
Integrated software stack
supporting task-based
programming model
Computing on a commercial
cutting-edge European-
developed CPU–GPU–FPGA
heterogeneous hardware
substrate and FPGA-based
Dataflow Engines (DFE)
Three use-cases (Smart
home/city, AI, health) to
test the integrated stack
Approach
5x decrease in Mean
Time to Failure
through energy-
efficient software-
based fault
tolerance.
Size reduction of the
trusted computing
base by at least one
order of magnitude.
5x times increase in
FPGA designer
productivity through
design of novel
features for hardware
design using dataflow
languages.
Use Cases
Healthcare
Will demonstrate not
only a decrease in
energy consumption
but also an increase
in healthcare
application
resilience and
security.
Machine Learning
Will improve energy
efficiency by
employing
accelerators and
tuning the accuracy
of computations at
runtime using CNN
and LSTM.
IoT for smart
homes and cities
The LEGaTO project
software-hardware
framework for the IoT
will demonstrate ease
of programming and
energy savings in
smart homes and smart
cities applications.
Chalmers is enhancing the XiTAO runtime1
as a LEGaTO Backend Scheduler
XiTAO & Energy
Up to 90%
Energy Savings
1
https://github.com/mpericas/xitao
●
Reduces Overheads
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Reduces Parallel
Slackness
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Destructive Interference
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Fine-grained parallelism
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Overheads
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Work-time Inflation
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Improves Parallel Slackness
●
Bulk creation of parallelism
●
Interference-freedom
●
Constructive sharing
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