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Name: XC5VLX330-2FF1760C
Brand: AMD Xilinx
SKU: XC5VLX330-2FF1760C
Price: 2.40 USD
Availability: InStock
Rating: 5 (2 reviews)

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AMD Xilinx XC5VLX330-2FF1760C

Part No.:: XC5VLX330-2FF1760C

Manufacturer:: AMD Xilinx

Package:: 1760-BBGA, FCBGA

Datasheet:: XC5VLX330-2FF1760C.pdf

Description:: IC FPGA 1200 I/O 1760FCBGA

In Stock:: 2308

Quantity:: Add To Cart

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XC5VLX330-2FF1760C Specifications

Package/Case:
1760-BBGA, FCBGA
Packaging:
Tray
Series:
Virtex®-5 LX
ProductStatus:
Active
NumberofLABs/CLBs:
25920
NumberofLogicElements/Cells:
331776
TotalRAMBits:
10616832
NumberofI/O:
1200
NumberofGates:
-
Voltage-Supply:
0.95V ~ 1.05V
MountingType:
Surface Mount
OperatingTemperature:
0°C ~ 85°C (TJ)

XC5VLX330-2FF1760C this integrated circuit is available in factory sealed anti static packs. at icwhale.com. Please read product page below detail information. including XC5VLX330-2FF1760C price, data-sheet, in-stock availability, technical difficulties. Also. Quickly Enter the access of compare listing to find out replaceable electronic parts. If you want to retrieve comprehensive data for XC5VLX330-2FF1760C to optimize the supply chain (including cross references, life-cycle, parametric, counterfeit risk, obsolescence managements forecasts), please contact to our Tech-supports team.

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XC5VLX330-2FF1760C information

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XC5VLX330-2FF1760C application case analysis in emerging technology fields

XC5VLX330-2FF1760C is a high-performance FPGA (Field-Programmable Gate Array) manufactured by Xilinx, offering extensive capabilities for accelerating various computational tasks and enabling intelligent solutions.

Application Scenario:

In image processing applications, XC5VLX330-2FF1760C can be utilized to enhance the efficiency and intelligence of real-time image recognition systems.

System Architecture:

To implement a real-time image recognition system using XC5VLX330-2FF1760C, consider the following architecture:

1. Image Acquisition:

Interface the FPGA with image sensors, such as CMOS or CCD cameras, to capture images. Utilize high-speed serial interfaces or parallel interfaces compatible with the sensor specifications.

2. Pre-processing:

Implement pre-processing algorithms on the FPGA to enhance image quality, remove noise, and perform tasks like color space conversion, edge detection, or image resizing.

3. Feature Extraction:

Utilize the FPGA's parallel processing capabilities to extract relevant features from the pre-processed images. Implement algorithms like convolutional neural networks (CNNs) or feature detectors to identify key patterns or objects.

4. Classification:

Deploy machine learning models on the FPGA to classify the extracted features and recognize objects within the images. Implement algorithms such as support vector machines (SVM), k-nearest neighbors (KNN), or deep learning networks.

5. Decision Making:

Based on the classification results, make real-time decisions or trigger actions. This could involve sending alerts, controlling actuators, or interfacing with higher-level systems for further processing.

Benefits:

- Parallel Processing: XC5VLX330-2FF1760C offers extensive parallel processing capabilities, enabling high-speed computation of image processing tasks and accelerating real-time performance.

- Flexibility: Being a field-programmable device, XC5VLX330-2FF1760C allows for flexibility in algorithm implementation and can be reconfigured to adapt to evolving application requirements.

- Integration: The FPGA can be integrated into existing hardware systems or embedded platforms, facilitating seamless integration with other components and peripherals.

- Power Efficiency: By offloading computationally intensive tasks to the FPGA, overall system power consumption can be reduced compared to traditional CPU-based approaches.

- Scalability: XC5VLX330-2FF1760C offers scalability in terms of processing power and resource utilization, allowing for the implementation of complex image processing algorithms and supporting future enhancements.

Considerations:

- Resource Utilization: Optimize resource utilization on the FPGA to ensure efficient use of available logic elements, memory blocks, and DSP slices.

- Algorithm Optimization: Implement optimized algorithms and data structures to maximize performance and minimize resource usage, considering the limitations of the FPGA architecture.

- System Integration: Ensure compatibility and proper interfacing with external components, such as image sensors, memory modules, and communication interfaces.

- Real-Time Constraints: Design the system to meet real-time processing requirements, considering factors like latency, throughput, and timing constraints.

- Testing and Validation: Thoroughly test the image recognition system to validate performance, accuracy, and reliability under various operating conditions and input scenarios.

XC5VLX330-2FF1760C FAQ

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6. What is the process for return or replacement of XC5VLX330-2FF1760C?

All goods will implement Pre-Shipment Inspection (PSI), selected at random from all batches of your order to do a systematic inspection before arranging the shipment. If there is something wrong with the XC5VLX330-2FF1760C we delivered, we will accept the replacement or return of the XC5VLX330-2FF1760C only when all of the below conditions are fulfilled:

(1)Such as a deficiency in quantity, delivery of wrong items, and apparent external defects (breakage and rust, etc.), and we acknowledge such problems.

(2)We are informed of the defect described above within 90 days after the delivery of XC5VLX330-2FF1760C.

(3)The PartNo is unused and only in the original unpacked packaging.

Two processes to return the products:

(1)Inform us within 90 days

(2)Obtain Requesting Return Authorizations