XC5VLX85-1FFG676I_XC5VSX240T-1FF1738C Introduction

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.

Xilinx devices enable high power efficiency for all product portfolios, including Spartan-6 series and 7 series, UltraScale™ and UltraScale+™ FPGAs and SoCs, through select silicon processes and power architectures. Below are details on specific product portfolio capabilities, silicon process advantages and benchmark comparisons. With each product generation, Xilinx continues to enhance its power-saving features, including process improvements, architectural innovations, voltage scaling strategies, and advanced software optimization strategies. Power estimates, thermal models, full software support and demo boards are now publicly available for all product families.

XC5VLX85-1FFG676I_XC5VSX240T-1FF1738C

XC4VLX80-11FF1148I

As human language writing forms have evolved, thousands of unique character systems have developed. Plus case (uppercase/lowercase/full small/small case), italic (Italian/Roman), scale (horizontal scale), weight, specified size (display/text), squiggly, serif (Generally divided into serifs and sans-serifs), this number can scale to millions, making text recognition an exciting professional discipline in the field of machine learning.

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.

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.

XC5VLX85-1FFG676I_XC5VSX240T-1FF1738C

XC4VLX40-10FF668C

XC6VLX130T-2FF784I XC6VLX130T-3FFG784C XC6VLX195T-1FF784I XC6VLX130T-2FFG1156C XC6VLX130T-2FFG784C XC6VLX130T-2FF484I XC6VLX130T-2FFG1156I XC6VLX130T-1FF484I XC6VLX130T-3FF784C XC6VLX130T-1FFG1156I XC6VLX130T-1FFG1156C XC6VLX130T-3FFG1156C XC6VLX195T-1FF1156C XC6VLX130T-3FF484C XC6VLX130T-3FFG484C XC6VLX130T-2FF484C XC5VTX240T- 2FF1759I XC5VTX240T-2FFG1759C XC5VTX240T-2FFG1759I XC6VLX130T-1FFG484C XC6VLX130T-1FFG484I.

XC4VLX160-10FF1513C XC4VLX15-11FFG668I XC4VLX160-10FFG1148C XC4VLX160-10FF1148I XC4VLX15-12SF363C XC4VLX160-10FF1513I XC4VLX160-10FF1148C XC4VLX15-12FFG668C XC4VLX15-11SFG363C XC4VLX160-11FFG1513C XC4VLX15-12FF668C XC4VLX15-11SF363I XC4VLX15-11FFG668C XC4VLX15-11SFG363I XC4VLX15-11SF363C XC4VLX160-12FFG1513C XC4VLX200- 10FF1513C XC4VLX200-10FF1513I XC4VLX160-10FFG1513C XC4VLX160-11FFG1513I.

XC6VLX240T-1FFG784I XC6VLX240T-2FF1156C XC6VLX365T-1FFG1759C XC6VLX240T-1FFG784C XC6VLX240T-3FF784C XC6VLX240T-3FFG784C XC6VLX365T-1FF1759C XC6VLX240T-3FFG1156C XC6VLX365T-2FF1156C XC6VLX365T-2FF1759I XC6VLX240T-3FFG1759C XC6VLX365T-2FF1156I XC6VLX550T-1FFG1760I XC6VLX550T-2FF1760C XC6VLX550T-2FF1759I XC6VLX550T-1FFG1759C XC6VLX365T- 1FF1759I XC6VLX365T-1FF1156C XC6VLX365T-1FF1156I XC6VLX550T-2FF1759C.

XC6VLX75T-1FF784C XC6VLX75T-1FF484I XC6VLX75T-1FF484C XC6VLX75T-3FFG784C XC6VLX75T-3FFG484C XC6VLX760-1FF1760I XC6VLX760-1FF1760C XC6VLX550T-2FFG1760C XC6VLX760-2FF1760C XC6VLX550T-2FFG1759C XC6VLX550T-2FFG1759I XC6VLX75T-2FF784I XC6VLX75T-2FF784C XC6VLX75T-2FFG484I XC6VLX75T-2FFG484C XC6VLX75T-2FFG784I XC6VLX75T- 2FFG784C XC6VLX75T-3FF784C XC6VLX75T-3FF484C XC6VLX365T-1FFG1156C XC6VLX365T-1FFG1759I XC6VLX365T-2FF1759C XC6VLX365T-1FFG1156I .

XC5VLX85-1FFG676I_XC5VSX240T-1FF1738C

While the CvMat and IplImage types are more focused on "images", OpenCV is optimized for image operations (scaling, single-channel extraction, image thresholding, etc.) The common data containers related to image operations in OpenCV are Mat, CvMat and IplImage. These three types can represent and display images. However, the Mat type focuses on calculation and is highly mathematical.

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.