The document outlines a low energy toolset aimed at optimizing OmpSS for energy-efficient heterogeneous computing, specifically using a combination of CPU, GPU, and FPGA technologies. Key use cases demonstrate significant gains in energy efficiency and performance across various applications, including smart cities, healthcare, and machine learning. The initiative aims to enhance fault tolerance, security, and programmer productivity while reducing total cost of ownership in microserver clusters.

1. A low energy toolset
for heterogeneous
computing

2. LEGaTO Ambition
• Optimizing OmpSs to support energy-efficiency
• Working on RECS|Box hardware with
CPU + GPU + FPGA + FPGA-based Dataflow Engines (DFE)
Create software stack-support for energy-efficient heterogeneous computing
Main goal: energy efficiency
• Fault tolerance
• Security
• Programmer productivity

3. Boosted Use-Cases
Smart City
7x gain in energy efficiency
using FPGAs
Health Care
822x speedup using
FPGAs, enabling a new
world of Biomarker
analysis
Machine Learning
up to 16x gain in energy
efficiency and
performance on the same
hardware, using the
EmbeDL optimizer

4. Smart Mirror
• Image: Object, face and gestures
• Speech with DeepSpeech
Displays personalized information
All detections simultaniously
• Start: 16 FPS at 500 W
• Now: 25 FPS at 400 W
• Goal: 10 FPS at 50 W
Local machine learning frameworks

5. Cluster Server
• 56-63% less TCO over 5 years
• Heterogeneous, flexible and scalable microserver cluster
• Turnkey appliances optimised to relevant classes of applications
X86 and ARM CPU, GPU, FPGA

6. TCO reduced by > 50 % after 5 years
Reduced TCO with RECS®|Box Deneb
 Lowered upgrade costs by replacing
only the microservers in regular
upgrade cycles
 Reduced data centre footprint
through ultra-high density of
computation power
 Less energy costs by superior energy-
efficiency

7. LEGaTO Partners