XC5VLX85-2FF1153I_XC5VSX240T-3FFG1738C Introduction

It was the largest M&A transaction in the United States at the time and the largest acquisition in ADI's history. In July 2020, U.S. chip giant Analog Devices Inc (ADI) announced that it plans to acquire rival Maxim Integrated Products for $20.9 billion in an all-stock deal to boost its presence in companies including telecommunications. capabilities in multiple industries.

Under the slowdown of Moore's law, Dennard's scaling law and Amdahl's law are close to the bottleneck, Moore even gave an "antidote", that is, "heterogeneous computing", which is now the solution of heterogeneous CPUs and accelerators. "Golden Age". So far in semiconductor development, the inevitable fact is that Moore's Law is slowing down.

XC5VLX85-2FF1153I_XC5VSX240T-3FFG1738C

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However, with opportunities come challenges. AI inference, the process of using trained machine learning algorithms to make predictions, whether deployed in the cloud, edge, or on-device, requires excellent processing performance within a tight power budget. The prevailing view is that this requirement cannot be met by CPUs alone, and that some form of computational acceleration is needed to handle AI inference workloads more efficiently.

It is a preconfigured, ready-to-run image for executing Dijkstra's shortest path search algorithm on Amazon's FGPA-accelerated F1. The GZIP accelerator provides hardware-accelerated gzip compression up to 25 times faster than CPU compression. The resulting archive conforms to the RFC 1952 GZIP file format specification. Go language to FPGA platform builds custom, reprogrammable, low-latency accelerators using software-defined chips. GraphSim is a graph-based ArtSim SSSP algorithm.

Softnautics took the Xilinx Vitis AI stack and used the software to provide acceleration to develop hybrid applications while implementing LSTM functionality for efficient sequence prediction by porting/migrating TensorFlow-lite to ARM. Image pre-processing/post-processing is implemented by Vivado using HLS, while Vitis's role is to perform inference using Connected Text Proposal Network (CTPN). It runs on the processing side (PS) using the N2Cube software. Ultimately, Softnautics uses the solution for real-time scene text detection in video pipelines and uses a robust dataset to refine the model.

The dynamic power problem is solved with low-capacitance circuits and custom modules. The power consumption of the multipliers in the DSP block is less than 20% of the power consumption of the multipliers built in the FPGA fabric. Given the wide range of leakage current distributions that can result from manufacturing variations, low leakage current devices can be screened to effectively provide devices with core leakage power consumption below 60%. To reduce static power dissipation, longer-channel and higher-threshold transistors are also used across the board. A variety of power-driven design techniques are used in the design of FPGAs. Taking the Xilinx Virtex series as an example, because the configuration memory cells can occupy 1/3 of the number of transistors in the FPGA, a low leakage current "midox" transistor is used in this series to reduce the leakage current of the memory cells.

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XC4VLX160-12FF1148C XC4VLX160-12FF1513C XC4VLX160-12FFG1148C XC4VLX160-11FF1513I XC4VLX160-11FFG1148C XC4VLX160-11FFG1148I XC4VLX25-10FFG668I XC4VLX160-10FFG1513I XC4VLX160-11FF1148C XC4VLX160-11FF1148I XC4VLX160-11FF1513C XC4VSX25-11FF668C XC4VSX25-11FF668I XC4VSX25-11FFG668C XC4VSX25-11FFG668I XC4VSX25-10FF668C XC4VSX25- 10FF668I XC4VSX25-10FFG668C XC4VSX25-10FFG668I XC4VLX80-11FF1148I XC4VLX80-11FFG1148I XC4VLX80-12FF1148C XC4VLX80-12FFG1148C .

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Therefore, in the design of OpenCV with VivadoHLS, it is necessary to modify the input and output HLS synthesizable video design interface to the Video stream interface, that is, use the video interface provided by HLS to synthesize the function to realize AXI4 video stream to VivadoHLS in hls ::Mat<> type conversion. The VivadoHLS video processing library uses the hls::Mat<> data type, which is used to model the processing of video pixel streams, and is essentially equivalent to the hls::steam<> stream type, rather than stored in external memory in OpenCV. matrix matrix type.

In July 2016, Xilinx said it would become an all-programmable company within the next five years, using its strengths to help customers differentiate and target emerging areas such as cloud computing, Internet of Things, 5G wireless and embedded vision. This is an adaptive computing acceleration platform. At present, the main series of FPGA products include high-performance virtex series, mid-range kintex series and low-cost artix and spartan series. Cyrus defines it as a new product different from CPU, GPU and FPGA. In fact, in 2014, Xilinx began work on a new generation of products that debuted in early 2018.