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Name: XCVU080-1FFVD1517I
Brand: AMD Xilinx
SKU: XCVU080-1FFVD1517I
Price: 2.40 USD
Availability: InStock
Rating: 5 (2 reviews)

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AMD Xilinx XCVU080-1FFVD1517I

Part No.:: XCVU080-1FFVD1517I

Manufacturer:: AMD Xilinx

Package:: 1517-BBGA, FCBGA

Datasheet:: XCVU080-1FFVD1517I.pdf

Description:: IC FPGA 338 I/O 1517FCBGA

In Stock:: 3459

Quantity:: Add To Cart

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XCVU080-1FFVD1517I Specifications

Package/Case:
1517-BBGA, FCBGA
Packaging:
Bulk
Series:
Virtex® UltraScale™
ProductStatus:
Active
NumberofLABs/CLBs:
55714
NumberofLogicElements/Cells:
975000
TotalRAMBits:
51200000
NumberofI/O:
338
NumberofGates:
-
Voltage-Supply:
0.922V ~ 0.979V
MountingType:
Surface Mount
OperatingTemperature:
-40°C ~ 100°C (TJ)

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XCVU080-1FFVD1517I information

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XCVU080-1FFVD1517I application case analysis in emerging technology fields

XCVU080-1FFVD1517I is a high-performance FPGA (Field-Programmable Gate Array) device designed by Xilinx, offering extensive capabilities for accelerating complex computational tasks and enabling intelligent applications.

Application Scenario:

In the field of artificial intelligence and machine learning, XCVU080-1FFVD1517I can be leveraged to accelerate deep learning inference tasks, such as image recognition, natural language processing, and autonomous driving.

System Architecture:

To implement a deep learning inference system using XCVU080-1FFVD1517I, the following components are typically integrated:

1. Neural Network Model:

Develop or select a pre-trained neural network model suitable for the target application, such as convolutional neural networks (CNNs) for image recognition or recurrent neural networks (RNNs) for natural language processing.

2. FPGA Accelerator:

Utilize the programmable logic resources of XCVU080-1FFVD1517I to implement hardware accelerators for neural network inference, enabling parallel processing of large datasets and complex computations.

3. Interface:

Integrate communication interfaces, such as PCIe or Ethernet, to enable data transfer between the FPGA-based accelerator and the host system or external devices, facilitating real-time inference and data exchange.

4. Memory Interface:

Connect external memory devices, such as DDR4 SDRAM, to provide ample storage for neural network parameters, input data, and intermediate results, optimizing performance and enabling scalability.

5. Power Management:

Implement efficient power supply and management solutions to ensure stable operation of XCVU080-1FFVD1517I and associated components, minimizing power consumption and heat generation.

Advantages:

Utilizing XCVU080-1FFVD1517I in deep learning inference applications offers several benefits:

- High Performance: The FPGA's parallel processing architecture enables accelerated inference tasks, achieving low latency and high throughput for real-time applications.

- Flexibility: The programmable nature of FPGAs allows for customization and optimization of neural network implementations, adapting to evolving algorithms and application requirements.

- Power Efficiency: By leveraging hardware acceleration, XCVU080-1FFVD1517I can achieve high computational efficiency while consuming less power compared to traditional CPU or GPU-based approaches.

- Scalability: FPGA-based solutions offer scalability to accommodate growing computational demands, supporting larger models, and datasets without significant performance degradation.

- Real-Time Responsiveness: The parallel processing capabilities of FPGAs enable real-time inference, making them suitable for latency-sensitive applications like autonomous vehicles and industrial automation.

Considerations:

When deploying XCVU080-1FFVD1517I in deep learning inference systems, it's important to consider:

- Resource Utilization: Optimize FPGA resource utilization to maximize performance and minimize hardware overhead, considering factors such as logic utilization, memory bandwidth, and routing congestion.

- Algorithm Efficiency: Implement efficient neural network algorithms and optimizations tailored to the FPGA architecture to fully leverage its computational capabilities and minimize latency.

- System Integration: Ensure seamless integration of the FPGA-based accelerator with the host system or edge devices, addressing compatibility, communication protocols, and data transfer bandwidth.

- Development Tools: Use robust development tools and frameworks, such as Vivado HLS and TensorFlow with FPGA support, to streamline the design, verification, and deployment of FPGA-based inference systems.

- Testing and Validation: Thoroughly test and validate the deployed system to verify accuracy, reliability, and performance across various datasets and operating conditions, ensuring consistent inference results.

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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.

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