XC7K410T-2FBG676C_XC7K420T-L2FFG1156E Introduction

So far in semiconductor development, the inevitable fact is that Moore's Law is slowing down. 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".

. Xilinx, which has a market value of about $26 billion, has gained about 9 percent this year, slightly ahead of the S&P 500's 7 percent gain. AMD's stock price has risen 89% this year, and its current market value exceeds $100 billion, thanks to the new crown epidemic working from home to increase market demand for PCs, game consoles and other devices that use AMD chips.

XC7K410T-2FBG676C_XC7K420T-L2FFG1156E

XC7K160T-2FBG676I

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 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.

Compared to the previous platform, the system-level performance per power has been improved by 4 times. It supports Xilinx Vitis AI, which provides extensive capabilities for building AI inference using accelerated libraries. In addition, it provides excellent high-level synthesis (HLS) capabilities. Softnautics selected the Xilinx Ultrascale+ platform because it offers the best in application processing and FPGA acceleration.

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.

Today, Xilinx's rich and powerful platform supports 70% of new developments, leading the way in FPGA-based system design. Softnautics chose Xilinx technology to implement this solution because it integrates both the Vitis™ AI stack and powerful hardware capabilities.

XC7K410T-2FBG676C_XC7K420T-L2FFG1156E

XC7K325T-1FBG900I

XCS30XL-6VQ100C XCS30XL-6TQG144I XCS30XL-6TQG144C XCS30XL-6TQ144I XCS30XL-6TQ144C XCS30XL-6TQ144 XCS30XL-6PQG208C XCS30XL-6PQ208I XCS30XL-6PQ208C XCS30XL-6CSG280I XCS30XL-6CS280I XCS30XL-6CS280C XCS30XL-6BGG256I XCS30XL-6BG256I 。

XCV200-5BGG256I XCV200-5BGG256C XCV200-5BG352I XCV200-5BG352C XCV200-5BG256I XCV200-5BG256C XCV200-4PQG240I XCV200-4PQG240C XCV200-4PQ240I XCV200-4PQ240C XCV200-4PQ240 XCV200-4PQ240 XCV200-4FGG456I XCV200-4FGG456C XCV200-4FGG256I XCV200-4FGG256C XCV200- 4FG456I XCV200-4FG456C.

XC7K160T-1FFG676I XC7K160T-1FFG676I XC7K160T-1FFG676C XC7K160T-1FFG676C XC7K160T-1FFG676ACX XC7K160T-1FF676I XC7K160T-1FF676C XC7K160T-1FBG676I XC7K160T-1FBG676C XC7K160T-1FBG484I XC7K160T-1FBG484I XC7K160T-1FBG484C XC7K160T-1FB676I XC7K160T-1FB676C XC7K160T-1FB484I XC7K160T-1FB484C XC7K160T 。

XCS30XL-6BG256C XCS30XL-5VQG100I XCS30XL-5VQG100C XCS30XL-5VQ84I XCS30XL-5VQ84C XCS30XL-5VQ280I XCS30XL-5VQ280C XCS30XL-5VQ256I XCS30XL-5VQ256C XCS30XL-5VQ240I XCS30XL-5VQ240C XCS30XL-5VQ208I XCS30XL-5VQ208C XCS30XL-5VQ144I XCS30XL-5VQ144C XCS30XL-5VQ100I 。

XC7K410T-2FBG676C_XC7K420T-L2FFG1156E

As the current chip manufacturing process becomes more and more complex, the chip design becomes more and more complex, the cost of chip designers soars, and the risk of chip streaming is further increased. The need to reduce chip cost, reduce chip risk and shorten time to market will further surge.

Intel's 10nm is still delayed, allowing Xilinx to dominate the FPGA market after acquiring Altera, in addition to the cloud market that Intel is focusing on. The competition between FPGAs and ASICs will continue. Apparently this applies to Intel and Nvidia. However, at 7nm, FPGA speed and density are greatly increased, and power consumption is also lower, so this competitive landscape may change, especially for ASICs and FPGAs. The introduction of ACAP will help Xilinx compete with higher-level competitors in new markets. Split the SoC prototyping and emulation market. Flexibility and adaptability are the main selling points of ACAP. Especially in the era of artificial intelligence, Xilinx also hopes to realize the future of Intel and Nvidia through this advantage.