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An overview of the 2020 single event effects FPGA effort performed by the NASA Electronic Parts and Packaging Group is presented. Information includes Heavy-ion data for the following FPGA devices: Xilinx Kintex-UltraScale and Microsemi PolarFire. Future work is presented.
Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that for high accuracy targets, our GPU implementation yields 5-9x speedup over CSR, and up to a 2x speedup over cuML.
This paper describes new software simulation code for predicting single event upset data from measured heavy ion data, using methods, code, and algorithms already reported in the open literature. The measured data that is used to compare against the results of this new simulation code has also been reported in the open literature (R. Koga, et al). The new code is not provided as part of this paper, only the methodology used in generating the code. This paper presents results of basic research, not design-to information, and is representative of other papers reported in the open literature (see paper references). Therefore, the content of this paper is suitable for being made publicly available at the IEEE conference and the resulting IEEE journal.
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The Address in Real Time Data Driver Card (ADDC) is designed to transmit the trigger data in the Micro-Mesh Gaseous Structure (Micromegas) detector of the ATLAS New Small Wheel (NSW) upgrade. The Address in Real Time (ART) signals are generated by the front-end ASIC, named VMM chip, to indicate the address of the first above-threshold event. A custom ASIC (ART ASIC) is designed to receive the ART signals from the VMM chip and implement the hit-selection. The processed data from the ART ASIC will be transmitted out of the NSW through the GBTx serializer, the unidirectional Versatile Twin Transmitter (VTTx) and fiber optical links. The ART signal is critical for the ATLAS experiment trigger selection thus the performance and stability of the ADDC is very important. To ensure extensive testing of the ADDC, an FPGA Mezzanine Card (FMC) based testing platform and a special designed firmware/software are developed. We find that this test platform works with the commercial Xilinx VC707 FPGA development kit, independent of the other electronics of the NSW it can test all the functions of the ADDC and its long-term stability. This paper will introduce the design, testing procedure and results of the ADDC and the FMC testing platform.
What did my team set out to accomplish:? Can I put a custom Machine Learning Model on FPGA?? Can I analyze network traffic in real time?? Can a QSFP port be used with an FPGA?? Does a visual representation of the latent space enhance our understanding of network traffic?? What is QSFP QSFP (Quad Small Form-Factor Pluggable)? QSFP supports transfer speeds generally up to 100Gb/s? Runs 4 parallel lines running up to 28 Gb/s? Why the latent space is important to our project ?Latent space is the compressed mapping of data points in a non-linear fashion? Create an understanding of the relationship of data collected? Can represent that relationship of a single network packet in 3 points (X, Y, Z) Project Outline FPGA? Custom Xilinx Petalinux Image for the operating system? Python program to collect packets and run them through the DPU (Data Processing Unit)? The program then sends the information over a socket to a computer? Display Program? Python Program that collects the information sent from the FPGA and display it in a graph
Progress on NASA’s Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project continues, specifically with this development of the Descent and Landing Computer (DLC). One of the DLC’s primary contributions as a SPLICE technology is its implementation of algorithms and operation of sensors to autonomously guide a spacecraft in performing more precise and safer landings on celestial bodies such as the Moon and Mars [1]. The second iteration of the DLC is known as the Engineering Test Unit (ETU) and one of its desired functionalities is the ability to preserve the fidelity of the system’s boot file through the use of memory scrubbing [2]. The ETU has two primary boards, one for housing a Multi-Processor System on a Chip (MPSoC) and the other for housing a Xilinx Kintex Ultrascale FPGA*. To emulate a memory scrubbing function implemented on the ETU’s FPGA board, the design and testing of a software application was performed on a Xilinx KCU105 FPGA evaluation board. The memory scrubbing application had to meet certain key criteria such as (1) properly utilize with the flash memory’s Serial Peripheral Interface (SPI) to read, write, and erase flash memory properly, (2) be able to detect arbitrarily large or small amounts of bit-errors, (3) be able to correct all detected errors, and (4) perform memory scrubbing indefinitely and autonomously. A prototype implementation was constructed and tested, demonstrating successful detection and correction of bit errors in multiple configurations. In the form of burst errors or singular bit flips, and in amounts of errors ranging from one to fifteen (per 256 Bytes), the application was successful in preserving memory fidelity.
Most evolutionary approaches to fault recovery in FPGAs focus on evolving alternative logic configurations as opposed to evolving the intra-cell routing. Since the majority of transistors in a typical FPGA are dedicated to interconnect, nearly 80% according to one estimate, evolutionary fault-recovery systems should benefit hy accommodating routing. In this paper, we propose an evolutionary fault-recovery system employing a genetic representation that takes into account both logic and routing configurations. Experiments were run using a software model of the Xilinx Virtex FPGA. We report that using four Virtex combinational logic blocks, we were able to evolve a 100% accurate quadrature decoder finite state machine in the presence of a stuck-at-zero fault.
This slide presentation reviews the single event upset static testing of the Virtex II field programmable gate arrays (FPGA) that were tested in protons and heavy-ions. The test designs and static and dynamic test results are reviewed.
An increasing number of parts are designed with digital control interfaces, including phase shifters and variable attenuators. When designing an antenna array in which each antenna has independent amplitude and phase control, the number of digital control lines that must be set simultaneously can grow very large. Use of a parallel interface would require separate line drivers, more parts, and thus additional failure points. A convenient form of control where single-phase shifters or attenuators could be set or the whole set could be programmed with an update rate of 100 Hz is needed to solve this problem. A digital interface board with a field-programmable gate array (FPGA) can simultaneously control an essentially arbitrary number of digital control lines with a serial command interface requiring only three wires. A small set of short, high-level commands provides a simple programming interface for an external controller. Parity bits are used to validate the control commands. Output timing is controlled within the FPGA to allow for rapid update rates of the phase shifters and attenuators. This technology has been used to set and monitor eight 5-bit control signals via a serial UART (universal asynchronous receiver/transmitter) interface. The digital interface board controls the phase and amplitude of the signals for each element in the array. A host computer running Agilent VEE sends commands via serial UART connection to a Xilinx VirtexII FPGA. The commands are decoded, and either outputs are set or telemetry data is sent back to the host computer describing the status and the current phase and amplitude settings. This technology is an integral part of a closed-loop system in which the angle of arrival of an X-band uplink signal is detected and the appropriate phase shifts are applied to the Ka-band downlink signal to electronically steer the array back in the direction of the uplink signal. It will also be used in the non-beam-steering case to compensate for phase shift variations through power amplifiers. The digital interface board can be used to set four 5-bit phase shifters and four 5-bit attenuators and monitor their current settings. Additionally, it is useful outside of the closed-loop system for beamsteering alone. When the VEE program is started, it prompts the user to initialize variables (to zero) or skip initialization. After that, the program enters into a continuous loop waiting for the telemetry period to elapse or a button to be pushed. A telemetry request is sent when the telemetry period is elapsed (every five seconds). Pushing one of the set or reset buttons will send the appropriate command. When a command is sent, the interface status is returned, and the user will be notified by a pop-up window if any error has occurred. The program runs until the End Program button is depressed.
The data acquisition and control instrumentation cubicles room of the ITER tokamak will be irradiated with neutrons during the fusion reactor operation. A Virtex-6 FPGA from Xilinx (XC6VLX365T-1FFG1156C) is used on the ATCA-IO-PROCESSOR board, included in the ITER Catalog of I and C products - Fast Controllers. The Virtex-6 is a re-programmable logic device where the configuration is stored in Static RAM (SRAM), functional data stored in dedicated Block RAM (BRAM) and functional state logic in Flip-Flops. Single Event Upsets (SEU) due to the ionizing radiation of neutrons causes soft errors, unintended changes (bit-flips) to the values stored in state elements of the FPGA. The SEU monitoring and soft errors repairing, when possible, were explored in this work. An FPGA built-in Soft Error Mitigation (SEM) controller detects and corrects soft errors in the FPGA configuration memory. Novel SEU sensors with Error Correction Code (ECC) detect and repair the BRAM memories. Proper management of SEU can increase reliability and availability of control instrumentation hardware for nuclear applications. The results of the tests performed using the SEM controller and the BRAM SEU sensors are presented for a Virtex-6 FPGA (XC6VLX240T-1FFG1156C) when irradiated with neutrons from the Portuguese Research Reactor (RPI), a 1 MW nuclear fission reactor operated by IST in the neighborhood of Lisbon. Results show that the proposed SEU mitigation technique is able to repair the majority of the detected SEU errors in the configuration and BRAM memories. (authors)
In this work we describe the first real world case study for the self-healing eDNA (electronic DNA) architecture by implementing the control and data processing of a Fourier Transform Spectrometer (FTS) on an eDNA prototype. For this purpose the eDNA prototype has been ported from a Xilinx Virtex 5 FPGA to an embedded system consisting of a PowerPC and a Xilinx Virtex 5 FPGA. The FTS instrument features a novel liquid crystal waveguide, which consequently eliminates all moving parts from the instrument. The addition of the eDNA architecture to do the control and data processing has resulted in a highly fault-tolerant FTS instrument. The case study has shown that the early stage prototype of the autonomous self-healing eDNA architecture is expensive in terms of execution time.
ATLAS LAr calorimeter will undergo its Phase-I upgrade during the long shutdown (LS2) in 2018, and a new LAr Trigger Digitizer Board (LTDB) will be designed and installed. Several commercial-off-the-shelf (COTS) multi-channel high-speed ADCs have been selected as possible backups of the radiation tolerant ADC ASICs for the LTDB. Here, to evaluate the radiation tolerance of these backup commercial ADCs, we developed an ADC radiation tolerance characterization system, which includes the ADC boards, data acquisition (DAQ) board, signal generator, external power supplies and a host computer. The ADC board is custom designed for different ADCs, with ADC drivers and clock distribution circuits integrated on board. The Xilinx ZC706 FPGA development board is used as a DAQ board. The data from the ADC are routed to the FPGA through the FMC (FPGA Mezzanine Card) connector, de-serialized and monitored by the FPGA, and then transmitted to the host computer through the Gigabit Ethernet. A software program has been developed with Python, and all the commands are sent to the DAQ board through Gigabit Ethernet by this program. Two ADC boards have been designed for the ADC, ADS52J90 from Texas Instruments and AD9249 from Analog Devices respectively. TID tests for both ADCs have been performed at BNL, and an SEE test for the ADS52J90 has been performed at Massachusetts General Hospital (MGH). Test results have been analyzed and presented. The test results demonstrate that this test system is very versatile, and works well for the radiation tolerance characterization of commercial multi-channel high-speed ADCs for the upgrade of the ATLAS LAr calorimeter. It is applicable to other collider physics experiments where radiation tolerance is required as well.
As part of the ATLAS Phase-I Upgrade, the global Feature EXtractor (gFEX) is one of several hardware modules designed to help maintain the ATLAS Level-1 trigger acceptance rate with the increasing Large Hadron Collider (LHC) luminosity and the increasing Pile-Up conditions. The gFEX is used to identify patterns of energy associated with the hadronic decays of high momentum Higgs, W & Z bosons, top quarks, and exotic particles in real time at the 40 MHz LHC bunch crossing rate. The board is required to receive coarse-granularity (Δη×Δphiv = 0.2×0.2 gTower) information from the entire ATLAS calorimeters on 276 optical fibers. A prototype v1 with one Xilinx ZYNQ FPGA, and one Vertex-7 FPGA for technology validation has been designed and tested in 2015. With the lessons learned from the prototype v1, a prototype v2 with three Vertex UltraScale FPGAs and one ZYNQ FPGA has been implemented to verify full functionalities of gFEX in 2016. Based on the prototype v2 design, a prototype v3, the final gFEX prototype, is implemented, which is an ATCA module consisting of three Vertex UltraScale+ FPGAs, one ZYNQ UltraScale+ SoC, and 35 MiniPODs. This board receives up to 300 fiber optical links from calorimeters and transmits trigger data on 96 links to the to the ATLAS Level-1 Topological trigger (L1Topo [1]) at the speed up to 12.8 Gb/s. There are also 24 electrical links on board for communication between two FPGAs with the speed up to 25.6 Gb/s. The performance of three prototype boards have been tested and evaluated. For the prototype v3 board, the high-speed optical links are stable at 12.8 Gb/s with Bit Error Ratio (BER) < 1 × 10 -15 . The low-latency parallel GPIO (General Purpose I/O) buses between FPGAs are stable at 1.12 Gb/s. The peripheral components of ZYNQ UltraScale+ SoC, such as 16 GB DDR4 DIMM, UART, SPI flashes, and Ethernet, have also been verified. Here, the test results of the prototype v3 board validate the gFEX technologies, architecture and full functionalities. Now the final production board is being produced.
Truly heterogeneous systems enable partitioned workloads to be mapped to the hardware that nets the best performance. However, current practice requires that inter-device communication between different vendors' hardware use host memory as an intermediary step. To date, there are no widely adopted solutions that allow accelerators to directly transfer data. A new cache-coherent protocol, CXL, aims to facilitate easier, fine-grained sharing between accelerators. In this work we analyze existing methods for designing heterogeneous applications that target GPUs and FPGAs working collaboratively, followed by an exploration to show the benefits of a CXL-enabled system. Specifically, we develop a test application that utilizes both an NVIDIA P100 GPU and a Xilinx U250 FPGA to show current communication limitations. From this application, we capture overall execution time and throughput measurements on the FPGA and GPU. We use these measurements as inputs to novel CXL performance models to show that using CXL caching instead of host memory results in a 1.31X speedup, while a more tightly-coupled pipelined implementation using CXL-enabled hardware would result in a speedup of 1.45X.
A flexible self-certifying pixel readout test system for testing the Endcap Timing Read-Out Chip (ETROC) has been developed for the CMS MIP Timing Detector (MTD), part of the upgrade for the HL-LHC. The system includes an FPGA-based (Xilinx KC705) test system that can take data from an emulator or up to four ETROC boards in telescope mode. A python based DAQ system simplifies configuration, calibration, and testing of the chips. The system provides an efficient & reliable solution for testing ETROC (ETROC2 since April 2023 with 16x16 pixels, full-size, full functionality prototype) and can be extended to other readout chips with similar architecture (AIDA integration is currently being explored).
Shown in this presentation are recent results for the upset susceptibility of the various types of memory elements in the embedded PowerPC405 in the Xilinx V2P40 FPGA. For critical flight designs where configuration upsets are mitigated effectively through appropriate design triplication and configuration scrubbing, these upsets of processor elements can dominate the system error rate. Data from irradiations with both protons and heavy ions are given and compared using available models.
Static random access memory (SRAM) upset rates in field programmable gate arrays (FPGAs) from the Xilinx Virtex 2 family have been tested for radiation effects on configuration memory, block RAM and the power-on-reset (POR) and SelectMAP single event functional interrupts (SEFIs). Dynamic testing has shown the effectiveness and value of Triple Module Redundancy (TMR) and partial reconfiguration when used in conjunction. Continuing dynamic testing for more complex designs and other Virtex 2 capabilities (i.e., I/O standards, digital clock managers (DCM), etc.) is scheduled.