XC6VLX365T-2FFG1759I_XC4VLX25-10SFG363I Introduction

On the other hand, AMD and Xilinx have been working closely together for a long time. A series of storage system-oriented IPs such as NVMe HA, NVMe TC and Embedded RDMA previously provided for AMD EPYC (Xiaolong) data center processors can help AMD build low latency The high-efficiency data path, thus realizing the efficient storage acceleration function of FPGA. In fact, a similar plot was staged as early as 2015, when Intel (Intel) acquired FPGA manufacturer Altera for $16.7 billion, and Altera also followed the trend for Intel's follow-up "CPU+xPU (GPU+FPGA+ASIC+ eASIC)” strategy provides the most solid foundation.

Power Estimation Power estimation is a critical step in low-power design. Using power estimation tools can be difficult to achieve accurately, but can still provide excellent guidance for power optimization by identifying high-power modules. As shown in Figure 1, certain external factors have an exponential effect on power consumption; small changes in the environment can cause significant changes in estimated power consumption. While the most accurate way to determine FPGA power consumption is through hardware measurements, power consumption estimates help identify high-power blocks and can be used to develop power budgets early in the design phase.

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For example, an image may need to be decompressed and scaled to meet the data input requirements of an AI model. Similar to AI inference implementations, non-AI preprocessing and postprocessing functions begin to require some form of acceleration. There is also a third challenge, and this is a lesser known one, which arises because AI inference cannot be deployed on its own. True AI deployments often require non-AI processing, either before or after AI capabilities. These traditional processing functions must run at the same throughput as the AI functions, with the same high performance and low power consumption.

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

It automatically adapts to Xilinx hardware based on software and algorithms without VHDL or Verilog expertise. Xilinx Vitis™ is a free, open-source development platform that encapsulates hardware modules into software-callable functions, while being compatible with standard development environments, tools, and open-source libraries.

In response to AMD's acquisition of Xilinx, the Wall Street Journal analyzed that AMD may use its high stock valuation as a bargaining chip to promote the transaction or delist Xilinx at a high price. AMD’s stock price has soared 89% this year, and its market value has now exceeded $100 billion to $101.568 billion.

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In 2013, the global FPGA market size was $4.563 billion, and by 2018, this figure will grow to $6.335 billion. In the global fpga market, Xilinx and altera have a market share of about 90%. Even in the chip design of cpu and other chip giants such as Xilinx and intel, they will first simulate on the fpga, and then perform the streaming processing of the chip, not to mention the AI-specific chips launched by many AI algorithm companies in recent years. . With the development of 5G and artificial intelligence, it is expected that by 2025, the scale of FPGAs will reach about 12.521 billion US dollars. On the one hand, chip manufacturers need to rely on FPGAs for simulation and prototyping; on the other hand, CPUs, GPUs, FPGAs, and ASICs (application-specific integrated circuits) are increasingly competing in the AI market. Sales revenue was US$850 million, an increase of 24% over the same period last year; net profit was US$241 million, an increase of 27% over the same period last year.

The company's dominant brands: XILINX, ALTERA, SAMSUNG, MICRON, HYNIX, NANYA, ISSI, INTEL, TI, MAXIM, ADI, POWER, DAVICOM, PLX, CYPRESS, MARVELL, AOS, ON, ST, NXP, IR, FREESCALE, NS, AVAGO, TOSHIBA, DIODES, RENESAS, ATMEL, etc...predominant brands. Introduction to Xilinx Agent After more than ten years of unremitting efforts, Aerospace Military Semiconductor Co., Ltd. has established good business relations with many well-known IC manufacturers and agents and OEMs in the United States, Britain, Germany, Japan, South Korea, and China. , Acting for the distribution of many well-known brand IC products in the world and domestic, customers all over the world.