Low-Precision Floating-Point for Efficient On-Board Deep Neural Network
  Processing
Cédric Gernigon Silviu-Ioan Filip Olivier Sentieys Clément Coggiola Mickaël Bruno
One of the major bottlenecks in high-resolution Earth Observation (EO) space
systems is the downlink between the satellite and the ground. Due to hardware
limitations, on-board power limitations or ground-station operation costs,
there is a strong need to reduce the amount of data transmitted. Various
processing methods can be used to compress the data. One of them is the use of
on-board deep learning to extract relevant information in the data. However,
most ground-based deep neural network parameters and computations are performed
using single-precision floating-point arithmetic, which is not adapted to the
context of on-board processing. We propose to rely on quantized neural networks
and study how to combine low precision (mini) floating-point arithmetic with a
Quantization-Aware Training methodology. We evaluate our approach with a
semantic segmentation task for ship detection using satellite images from the
Airbus Ship dataset. Our results show that 6-bit floating-point quantization
for both weights and activations can compete with single-precision without
significant accuracy degradation. Using a Thin U-Net 32 model, only a 0.3%
accuracy degradation is observed with 6-bit minifloat quantization (a 6-bit
equivalent integer-based approach leads to a 0.5% degradation). An initial
hardware study also confirms the potential impact of such low-precision
floating-point designs, but further investigation at the scale of a full
inference accelerator is needed before concluding whether they are relevant in
a practical on-board scenario.