Updated NeuronRain Documentation - http://neuronrain-documentation.re…

…adthedocs.io/en/latest/

index.rst

@@ -296,7 +296,7 @@ Psutils Dictionary Encoding of a process and Diff edit distance between two proc
 
 **Who can deploy NeuronRain?**
 
-Anyone interested in dynamic analytics driven kernel. For example, realtime IoT kernels operating on smart devices, autonomous driverless vehicles, robots, drones, embedded systems etc.,. There are already linux distros for drones and unmanned aerial vehicles (https://www.dronecode.org/) and automotives (Automotive Grade Linux - https://www.automotivelinux.org/). For example autonomous vehicles and drones have linux kernel drivers for LIDAR sensors for navigation which can be analytics driven. Linux kernel tree has support for LIDAR sensors and GARMIN GPS USB drivers (pulsedlight LIDAR driver - https://github.com/torvalds/linux/commits/master/drivers/iio/proximity/pulsedlight-lidar-lite-v2.c, GARMIN GPS USB drivers - http://elixir.free-electrons.com/linux/latest/source/drivers/usb/serial/garmin_gps.c). LIDAR sensor and GPS drivers can import kernel_analytics exported variables - from UAV autopilot, drone navigation for example. Present implementation of kernel_analytics driver in VIRGO32 and VIRGO64 reads /etc/kernel_analytics.conf by VFS kernel functions. In autonomous driving this file has to be overwritten in high frequency by machine learning userspace code. Intense File I/O in kernel modules is strongly advised against. Some realtime alternatives to this have been minimally implemented e.g perpetual reading of analytics variables from a streaming socket in a local or remote cloud node in kernelspace - something similar to Spark Streaming in Kernelspace. This would remove disk latency and necessity for storage of analytics variables - kernel_analytics driver reads the variables from socket and exports them kernelwide in an infinite loop. VIRGO64 kernel_analytics module has an optional function implemented to read stream of config variable-value pairs connecting to an analytics server and stored in a circular buffer exported kernelwide. For realtime low latency requirements viz., autonomous vehicles, patching linux kernel with realtime PREEMPT_RT (https://git.kernel.org/pub/scm/linux/kernel/git/rt/linux-rt-devel.git/tree/) is suggested (though this has not been tested).
+Anyone interested in dynamic analytics driven kernel. For example, realtime IoT kernels operating on smart devices, autonomous driverless vehicles, robots, drones, embedded systems etc.,. There are already linux distros for drones and unmanned aerial vehicles (https://www.dronecode.org/) and automotives (Automotive Grade Linux - https://www.automotivelinux.org/). For example autonomous vehicles and drones have linux kernel drivers for LIDAR sensors for navigation which can be analytics driven. Linux kernel tree has support for LIDAR sensors and GARMIN GPS USB drivers (pulsedlight LIDAR driver - https://github.com/torvalds/linux/commits/master/drivers/iio/proximity/pulsedlight-lidar-lite-v2.c, GARMIN GPS USB drivers - http://elixir.free-electrons.com/linux/latest/source/drivers/usb/serial/garmin_gps.c). LIDAR sensor and GPS drivers can import kernel_analytics exported variables - from UAV autopilot, drone navigation for example. Present implementation of kernel_analytics driver in VIRGO32 and VIRGO64 reads /etc/kernel_analytics.conf by VFS kernel functions. In autonomous driving this file has to be overwritten in high frequency by machine learning userspace code. Intense File I/O in kernel modules is strongly advised against. Some realtime alternatives to this have been minimally implemented e.g perpetual reading of analytics variables from a streaming socket in a local or remote cloud node in kernelspace - something similar to Spark Streaming in Kernelspace. This would remove disk latency and necessity for storage of analytics variables - kernel_analytics driver reads the variables from socket and exports them kernelwide in an infinite loop. VIRGO64 kernel_analytics module has an optional function implemented to read stream of config variable-value pairs connecting to an analytics server and stored in a circular buffer exported kernelwide. For realtime low latency requirements viz., autonomous vehicles, patching linux kernel with realtime PREEMPT_RT (https://git.kernel.org/pub/scm/linux/kernel/git/rt/linux-rt-devel.git/tree/) is suggested (though this has not been tested). NeuronRain is a generic machine learning, kernelspace cloud system calls and drivers for analytics powered linux fork-off which integrates cloud and machine learning features into kernel itself more than being IoT specific e.g ARM has a linux fork-off - https://github.com/ARM-software/linux and Machine Learning Library based on GoogLeNet Deep Learning - https://github.com/ARM-software/ComputeLibrary
 
 **How does NeuronRain compare against other Cloud IoT platforms?**