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Name: XCVU7P-1FLVB2104E
Brand: AMD Xilinx
SKU: XCVU7P-1FLVB2104E
Price: 2.40 USD
Availability: InStock
Rating: 5 (2 reviews)

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AMD Xilinx XCVU7P-1FLVB2104E

Part No.:: XCVU7P-1FLVB2104E

Manufacturer:: AMD Xilinx

Package:: 2104-BBGA, FCBGA

Datasheet:: XCVU7P-1FLVB2104E.pdf

Description:: IC FPGA 702 I/O 2104FCBGA

In Stock:: 3005

Quantity:: Add To Cart

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XCVU7P-1FLVB2104E Specifications

Package/Case:
2104-BBGA, FCBGA
Packaging:
Tray
Series:
Virtex® UltraScale+™
ProductStatus:
Active
NumberofLABs/CLBs:
98520
NumberofLogicElements/Cells:
1724100
TotalRAMBits:
260812800
NumberofI/O:
702
NumberofGates:
-
Voltage-Supply:
0.825V ~ 0.876V
MountingType:
Surface Mount
OperatingTemperature:
0°C ~ 100°C (TJ)

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XCVU7P-1FLVB2104E information

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XCVU7P-1FLVB2104E application case analysis in emerging technology fields

XCVU7P-1FLVB2104E is a high-capacity field-programmable gate array (FPGA) designed by Xilinx, offering advanced features for accelerating compute-intensive applications in various domains.

Application Scenario:

In the field of image processing and computer vision, XCVU7P-1FLVB2104E can be leveraged to perform real-time object detection and recognition tasks in video surveillance systems.

System Architecture:

To implement a real-time object detection system using XCVU7P-1FLVB2104E, the following architectural components can be employed:

1. Image Acquisition:

Utilize high-resolution cameras to capture video streams, and preprocess the images to enhance clarity and reduce noise. The processed frames are then fed into the FPGA for further analysis.

2. Object Detection Algorithm:

Develop or utilize pre-trained convolutional neural network (CNN) models optimized for FPGA deployment. These models are responsible for detecting objects within the video frames and providing bounding box coordinates and class labels.

3. FPGA Acceleration:

Deploy the object detection algorithm onto XCVU7P-1FLVB2104E to leverage its parallel processing capabilities. The FPGA's reconfigurable nature allows for efficient execution of complex computations, resulting in high throughput and low latency.

4. Integration with System Components:

Integrate the FPGA with other system components, such as microcontrollers or embedded processors, for coordinating tasks, managing I/O interfaces, and facilitating communication with external devices or networks.

5. Real-Time Response:

Optimize the FPGA firmware to ensure real-time processing of video streams, enabling timely detection and response to dynamic changes in the environment. Implement techniques such as pipelining and parallelism to maximize computational efficiency.

Benefits and Impact:

The integration of XCVU7P-1FLVB2104E in real-time object detection systems offers several advantages:

- High Performance: The FPGA's parallel processing architecture accelerates object detection tasks, enabling real-time performance even for high-resolution video streams.

- Flexibility: FPGA-based solutions are adaptable and can be reconfigured to accommodate evolving object detection algorithms or application requirements without hardware changes.

- Power Efficiency: By offloading compute-intensive tasks to the FPGA, overall system power consumption can be reduced compared to CPU-based solutions, making it suitable for battery-powered or energy-efficient applications.

- Scalability: The scalability of FPGA solutions allows for deployment in a wide range of applications, from small-scale surveillance systems to large-scale smart city deployments.

Considerations:

When designing and deploying XCVU7P-1FLVB2104E-based solutions, it's important to consider factors such as:

- Resource Utilization: Optimize FPGA resource utilization to maximize performance and minimize resource contention among different modules within the system.

- Algorithm Complexity: Balance the complexity of the object detection algorithm with the available FPGA resources and processing capabilities to ensure efficient execution.

- Integration Challenges: Address challenges related to interfacing the FPGA with other system components, such as data synchronization, communication protocols, and system latency.

- Validation and Testing: Thoroughly validate the FPGA-based object detection system under various scenarios to ensure accuracy, reliability, and robustness in real-world environments.

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6. What is the process for return or replacement of XCVU7P-1FLVB2104E?

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(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 XCVU7P-1FLVB2104E.

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