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+# EAS patches for linux-reneas layer
+
+This README file contains information on the contents of the
+meta-eas layer.
+
+Please see the corresponding sections below for details.
+
+### Dependencies
+-------------------------
+
+This layer depends on:
+
+  * URI: https://gerrit.automotivelinux.org/gerrit/AGL/meta-renesas-rcar-gen3<br>
+  layers: `meta-rcar-gen3`<br>
+  tag: `master`
+
+The machine feature `biglittle` must be set in order for these patches
+to be applied.
+If support in needed for another SoC, please add the machine feature
+`biglittle` and provide the relevant EAS patches for the linux kernel.
+
+### Patches
+-----------
+
+Please submit any patches against the meta-baylibre-agl-eas layer to the
+the maintainers:
+
+* Michael Turquette <mturquette@baylibre.com>
+* Frode Isaksen <fisaksen@baylibre.com>
+* Jerome Brunet <jbrunet@baylibre.com>
+
+## I. Description and provided packages:
+
+The layer provides Energy Aware Scheduling (EAS) patches for the linux-reneases kernel. 
+This package is an experimental utility to improve scheduling efficiency on big/LITTLE architecture.
+
++ Patched packages :
+	- linux-renesas: Add configuration flags and patches required for EAS.
+
+## II. Adding the meta-baylibre-agl-eas layer to your AGL build
+
+1. Download meta-agl-extra at `$AGL_TOP`
+2. Add `eas` to the feature of your AGL build<br>
+```shell
+source meta-agl/scripts/aglsetup.sh -m $MACHINE -b <your-other-features> eas
+```
+
+With this `meta-eas` will be added to your `conf/bblayers.conf`.
+
+## III. Background information:
+
+Several techniques for saving energy through various scheduler
+modifications have been proposed in the past, however most of the
+techniques have not been universally beneficial for all use-cases and
+platforms. For example, consolidating tasks on fewer cpus is an
+effective way to save energy on some platforms, while it might make
+things worse on others.
+
+This proposal, which is inspired by the Ksummit workshop discussions in
+2013 [1], takes a different approach by using a (relatively) simple
+platform energy cost model to guide scheduling decisions. By providing
+the model with platform specific costing data the model can provide a
+estimate of the energy implications of scheduling decisions. So instead
+of blindly applying scheduling techniques that may or may not work for
+the current use-case, the scheduler can make informed energy-aware
+decisions. We believe this approach provides a methodology that can be
+adapted to any platform, including heterogeneous systems such as ARM
+big.LITTLE. The model considers cpus only, i.e. no peripherals, GPU or
+memory. Model data includes power consumption at each P-state and
+C-state.
+
+## IV. Further reading:
+
+https://developer.arm.com/open-source/energy-aware-scheduling