DR300: XCube R&D’s latest multi-source data recorder


XCube Research and Development, a supplier in parallel data management, analytics and software infrastructure for automotive manufacturers and ADAS suppliers, announced today that in order to further support its Distributed Store, Search and Compute (DSSC) users, they are introducing the new XCube DR300 data recorder, capable of capturing a multitude of inputs and easily connect to multiple devices receiving the output.

The development challenges facing those in the autonomous driving space include the collection, aggregation, management and analysis of petabyte scale fragmented data sets in real time. XCube can provide both the hardware and software solution system to enable simplifying and streamlining this massive data management effort.

XCube’s new DR300 data recorder will solve the collection and porting of massive data such as 1000’s of hours of video needed for testing and safety training of autonomous vehicles. Available only to users of its backend software for the platform (DSSC) to manage the lifecycle of autonomous driving and ADAS devices, the new DR300 represents a new frontier in chip design and FPGA programming. It is the only data acquisition device that makes inputs common in the automotive industry, such as LVDS, FPDLink, and AHD, simple and easy to use.

XCube’s innovations in FPGA design finally make these different data streams modular, regardless of type, the user can easily convert the inputs to common standards such as GigE-Vision or USBVision. The input streams can also be sliced and split at the user’s behest, or even collected and batch converted to any number of familiar formats, such as the analytics-friendly MPEG.

Other features include the ability to record an input video stream without using CPU resources or impeding the user’s ability to run their own processing simultaneously.

XCube’s DSSC is an invaluable software support toolkit for large scale data aggregation, management, training and analysis which enables our customers to efficiently solve their development and provability challenges for the safety critical AI applications.

Source: XCube Research & Development