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Name: LFE2M35SE-6FN484C
Brand: Lattice Semiconductor Corporation
SKU: LFE2M35SE-6FN484C
Price: 2.40 USD
Availability: InStock
Rating: 5 (2 reviews)

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Lattice Semiconductor Corporation LFE2M35SE-6FN484C

Part No.:: LFE2M35SE-6FN484C

Manufacturer:: Lattice Semiconductor Corporation

Package:: 484-BBGA

Datasheet:: LFE2M35SE-6FN484C.pdf

Description:: IC FPGA 303 I/O 484FBGA

In Stock:: 3264

Quantity:: Add To Cart

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LFE2M35SE-6FN484C Specifications

Package/Case:
484-BBGA
Packaging:
Tray
Series:
ECP2M
ProductStatus:
Active
NumberofLABs/CLBs:
4250
NumberofLogicElements/Cells:
34000
TotalRAMBits:
2151424
NumberofI/O:
303
NumberofGates:
-
Voltage-Supply:
1.14V ~ 1.26V
MountingType:
Surface Mount
OperatingTemperature:
0°C ~ 85°C (TJ)

LFE2M35SE-6FN484C this integrated circuit is available in factory sealed anti static packs. at icwhale.com. Please read product page below detail information. including LFE2M35SE-6FN484C 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 LFE2M35SE-6FN484C 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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LFE2M35SE-6FN484C information

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LFE2M35SE-6FN484C application case analysis in emerging technology fields

LFE2M35SE-6FN484C is a field-programmable gate array (FPGA) chip known for its high performance and versatility, commonly used in applications requiring reconfigurable hardware acceleration.

Application Scenario:

In image processing and computer vision applications, LFE2M35SE-6FN484C can be utilized to accelerate complex algorithms such as convolutional neural networks (CNNs) for object detection and recognition tasks.

Circuit Design:

To design a smart surveillance system using LFE2M35SE-6FN484C, follow these steps:

1. Image Sensor Integration:

Connect an image sensor module, such as a CMOS or CCD camera, to the input pins of LFE2M35SE-6FN484C to capture live video feeds. Ensure compatibility and proper signal conditioning for optimal image quality.

2. FPGA Configuration:

Program LFE2M35SE-6FN484C with custom logic designs tailored to the specific image processing tasks. Utilize the FPGA's reconfigurable nature to adapt algorithms and optimize performance based on real-time processing requirements.

3. Object Detection:

Implement CNN algorithms within the FPGA fabric to analyze video frames for detecting objects of interest. Utilize parallel processing capabilities of LFE2M35SE-6FN484C to efficiently handle large volumes of image data.

4. Decision Making:

Integrate decision-making logic within the FPGA to classify detected objects and trigger appropriate actions, such as alert notifications or automated responses.

5. Integration with IoT:

Utilize communication interfaces available on LFE2M35SE-6FN484C, such as Ethernet or Wi-Fi, to connect the surveillance system to an IoT network for remote monitoring and control capabilities.

Considerations:

When designing the system, consider:

- Power Efficiency: Optimize FPGA configurations and algorithms to minimize power consumption while maintaining performance levels.

- Real-Time Processing: Implement efficient pipelining and parallel processing techniques to meet real-time processing requirements of surveillance applications.

- Scalability: Design the system with scalability in mind to accommodate future expansion and integration of additional sensors or functionalities.

- Security: Implement robust security measures to protect sensitive data and prevent unauthorized access to the surveillance system.

- Testing and Validation: Thoroughly test the system under various scenarios to ensure accurate object detection performance and reliability in real-world environments.

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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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