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Register a new userDevelopment of a scalable generic platform for adaptive optics real time control
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Added by
Avinash Surendran Mr.
Publication date
2015
fields
Physics
and research's language is
English
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The main objective of the present project is to explore the viability of an adaptive optics control system based exclusively on Field Programmable Gate Arrays (FPGAs), making strong use of their parallel processing capability. In an Adaptive Optics (AO) system, the generation of the Deformable Mirror (DM) control voltages from the Wavefront Sensor (WFS) measurements is usually through the multiplication of the wavefront slopes with a predetermined reconstructor matrix. The ability to access several hundred hard multipliers and memories concurrently in an FPGA allows performance far beyond that of a modern CPU or GPU for tasks with a well defined structure such as Adaptive Optics control. The target of the current project is to generate a signal for a real time wavefront correction, from the signals coming from a Wavefront Sensor, wherein the system would be flexible to accommodate all the current Wavefront Sensing techniques and also the different methods which are used for wavefront compensation. The system should also accommodate for different data transmission protocols (like Ethernet, USB, IEEE 1394 etc.) for transmitting data to and from the FPGA device, thus providing a more flexible platform for Adaptive Optics control. Preliminary simulation results for the formulation of the platform, and a design of a fully scalable slope computer is presented.
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We demonstrate a novel architecture for Adaptive Optics (AO) control based on FPGAs (Field Programmable Gate Arrays), making active use of their configurable parallel processing capability. SPARCs unique capabilities are demonstrated through an implementation on an off-the-shelf inexpensive Xilinx VC-709 development board. The architecture makes SPARC a generic and powerful Real-time Control (RTC) kernel for a broad spectrum of AO scenarios. SPARC is scalable across different numbers of subapertures and pixels per subaperture. The overall concept, objectives, architecture, validation and results from simulation as well as hardware tests are presented here. For Shack-Hartmann wavefront sensors, the total AO reconstruction time ranges from a median of 39.4us (11x11 subapertures) to 1.283 ms (50x50 subapertures) on the development board. For large wavefront sensors, the latency is dominated by access time (~1 ms) of the standard DDR memory available on the board. This paper is divided into two parts. Part 1 is targeted at astronomers interested in the capability of the current hardware. Part 2 explains the FPGA implementation of the wavefront processing unit, the reconstruction algorithm and the hardware interfaces of the platform. Part 2 mainly targets the embedded developers interested in the hardware implementation of SPARC.
The next generation of Adaptive Optics (AO) systems on large telescopes will require immense computation performance and memory bandwidth, both of which are challenging with the technology available today. The objective of this work is to create a future-proof adaptive optics platform on an FPGA architecture, which scales with the number of subapertures, pixels per subaperture and external memory. We have created a scalable adaptive optics platform with an off-the-shelf FPGA development board, which provides an AO reconstruction time only limited by the external memory bandwidth. SPARC uses the same logic resources irrespective of the number of subapertures in the AO system. This paper is aimed at embedded developers who are interested in the FPGA design and the accompanying hardware interfaces. The central theme of this paper is to show how scalability is incorporated at different levels of the FPGA implementation. This work is a continuation of Part 1 of the paper which explains the concept, objectives, control scheme and method of validation used for testing the platform.
The forthcoming Extremely Large Telescopes all require adaptive optics systems for their successful operation. The real-time control for these systems becomes computationally challenging, in part limited by the memory bandwidths required for wavefront reconstruction. We investigate new POWER8 processor technologies applied to the problem of real-time control for adaptive optics. These processors have a large memory bandwidth, and we show that they are suitable for operation of first-light ELT instrumentation, and propose some potential real-time control system designs. A CPU-based real-time control system significantly reduces complexity, improves maintainability, and leads to increased longevity for the real-time control system.
We propose a solution to the increased computational demands of Extremely Large Telescope (ELT) scale adaptive optics (AO) real-time control with the Intel Xeon Phi Knights Landing (KNL) Many Integrated Core (MIC) Architecture. The computational demands of an AO real-time controller (RTC) scale with the fourth power of telescope diameter and so the next generation ELTs require orders of magnitude more processing power for the RTC pipeline than existing systems. The Xeon Phi contains a large number (> 64) of low power x86 CPU cores and high bandwidth memory integrated into a single socketed server CPU package. The increased parallelism and memory bandwidth are crucial to providing the performance for reconstructing wavefronts with the required precision for ELT scale AO. Here, we demonstrate that the Xeon Phi KNL is capable of performing ELT scale single conjugate AO real-time control computation at over 1.0 kHz with less than 20 {mu}s RMS jitter. We have also shown that with a wavefront sensor camera attached the KNL can process the real-time control loop at up to 966 Hz, the maximum frame-rate of the camera, with jitter remaining below 20 {mu}s RMS. Future studies will involve exploring the use of a cluster of Xeon Phis for the real-time control of the MCAO and MOAO regimes of AO. We find that the Xeon Phi is highly suitable for ELT AO real time control.
Flares of known astronomical sources and new transient phenomena occur on different timescales, from sub-seconds to several days or weeks. The discovery potential of both serendipitous observations and multi-messenger and multi-wavelength follow-up observations could be maximized with a tool which allows for quickly acquiring an overview over both persistent sources as well as transient events in the relevant phase space. We here present COincidence LIBrary for Real-time Inquiry (Astro-COLIBRI), a novel and comprehensive tool for this task. Astro-COLIBRIs architecture comprises a RESTful API, a real-time database, a cloud-based alert system and a website (https://astro-colibri.com) as well as apps for iOS and Android as clients for users. The structure of Astro-COLIBRI is optimized for performance and reliability and exploits concepts such as multi-index database queries, a global content delivery network (CDN), and direct data streams from the database to the clients. Astro-COLIBRI evaluates incoming VOEvent messages of astronomical observations in real time, filters them by user-specified criteria and puts them into their MWL and MM context. The clients provide a graphical representation with an easy to grasp summary of the relevant data to allow for the fast identification of interesting phenomena and provides an assessment of observing conditions at a large selection of observatories around the world. We here summarize the key features of Astro-COLIBRI, the architecture and used data resources. We specifically provide examples for applications and use cases. Focussing on the high-energy domain, we showcase how Astro-COLIBRI facilitates the search for high-energy gamma-ray counterparts to high-energy neutrinos and scheduling of follow-up observations of a large variety of transient phenomena like gamma-ray bursts, gravitational waves, TDEs, FRBs, and others.
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