Two methods are presented for finding the top-k items in data streams using Field Programmable Gate Arrays (FPGAs). These methods deploy two variants of a novel accelerator architecture capable of extracting an approximate list of the topmost frequently occurring items in a single pass over the input stream without the need for random access. The first variant of the accelerator implements the well-known Probabilistic sampling algorithm by mapping its main processing stages to a hardware architecture consisting of two custom systolic arrays. The proposed architecture retains all the properties of this algorithm, which works even if the stream size is unknown at run time.The architecture shows better scalability compared to other architectures that are based on other stream algorithms. In addition, experimental results on both synthetic and real datasets, when implementing the accelerator on an Intel Arria 10 GX 1150 FPGA device, showed very good accuracy and significant throughput gains compared to the existing software and hardware-accelerated solutions. The second variant of the accelerator is specifically tailored for applications requiring higher accuracy, provided that the size of the stream is known at run time. This variant takes advantage of the embedded memory resources in an FPGA to implement a sketch-based filter that precedes the main systolic array in the accelerator's pipeline. This filter enhances the accuracy of the accelerator by pre-processing the stream to remove much of the insignificant items, allowing the accelerator to process a significantly smaller filtered stream.

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Because of SRAM sensitivity to radiation, SRAM-based FPGA systems deployed in harsh environments require error mitigation methods to reduce their overall downtime. This paper presents a fault-tolerant reconfigurable imaging system that relies on the DPR feature for correcting errors in the configuration memory and loading camera system IPs. The system reliability is evaluated by injecting faults in the FPGA configuration memory at runtime using the Xilinx SEM IP. The faults are injected internally using the Internal Configuration Access Port (ICAP), which is shared between the fault injection core and system parts. The results showed that 95% of the errors can by corrected automatically. This paper also proposes a fast-Built-in-Self-Test (BIST) mitigation technique to reduce the overall downtime in case of errors. This technique can reduce the recovery time by 80%. Moreover, Triple Modular Redundancy (TMR) is used to increase the overall reliability without significantly increasing the resource overhead.