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Name: LFE3-95EA-7LFN1156C
Brand: Lattice Semiconductor Corporation
SKU: LFE3-95EA-7LFN1156C
Price: 2.40 USD
Availability: InStock
Rating: 5 (2 reviews)

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Lattice Semiconductor Corporation LFE3-95EA-7LFN1156C

Part No.:: LFE3-95EA-7LFN1156C

Manufacturer:: Lattice Semiconductor Corporation

Package:: 1156-BBGA

Datasheet:: LFE3-95EA-7LFN1156C.pdf

Description:: IC FPGA 490 I/O 1156FBGA

In Stock:: 3484

Quantity:: Add To Cart

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LFE3-95EA-7LFN1156C Specifications

Package/Case:
1156-BBGA
Packaging:
Tray
Series:
ECP3
ProductStatus:
Active
NumberofLABs/CLBs:
11500
NumberofLogicElements/Cells:
92000
TotalRAMBits:
4526080
NumberofI/O:
490
NumberofGates:
-
Voltage-Supply:
1.14V ~ 1.26V
MountingType:
Surface Mount
OperatingTemperature:
0°C ~ 85°C (TJ)

LFE3-95EA-7LFN1156C this integrated circuit is available in factory sealed anti static packs. at icwhale.com. Please read product page below detail information. including LFE3-95EA-7LFN1156C 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 LFE3-95EA-7LFN1156C 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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LFE3-95EA-7LFN1156C information

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LFE3-95EA-7LFN1156C application case analysis in emerging technology fields

LFE3-95EA-7LFN1156C is an FPGA (Field-Programmable Gate Array) device renowned for its versatility and efficiency in various applications. It offers high performance and flexibility, making it ideal for complex digital systems requiring real-time processing capabilities.

Application Scenario:

In the domain of image processing and computer vision, LFE3-95EA-7LFN1156C can revolutionize the way algorithms are executed. Let's explore its application in enhancing object recognition systems.

System Architecture:

To implement an advanced object recognition system using LFE3-95EA-7LFN1156C, consider the following architecture:

1. Image Acquisition:

Utilize image sensors such as CMOS or CCD cameras to capture real-world scenes. Interface these sensors with LFE3-95EA-7LFN1156C through dedicated interfaces like MIPI CSI or parallel interfaces for high-speed data transfer.

2. Pre-processing:

Implement pre-processing algorithms within the FPGA to enhance image quality, remove noise, and normalize illumination variations. Use dedicated DSP blocks and custom logic to accelerate these operations.

3. Feature Extraction:

Deploy advanced feature extraction algorithms, such as convolutional neural networks (CNNs), within LFE3-95EA-7LFN1156C. Utilize its parallel processing capabilities to efficiently extract discriminative features from input images.

4. Object Classification:

Train and deploy machine learning models for object classification tasks. Implement these models within the FPGA fabric using techniques like quantization and pruning to optimize resource utilization and inference speed.

5. Real-time Decision Making:

Utilize the low-latency and high-throughput nature of LFE3-95EA-7LFN1156C to enable real-time decision-making capabilities. Implement decision fusion algorithms to integrate information from multiple sensors and improve system robustness.

Advantages:

- LFE3-95EA-7LFN1156C provides high computational throughput, enabling real-time processing of large volumes of image data.

- Its reconfigurable nature allows for algorithm optimization and adaptation to changing application requirements.

- With integrated DSP blocks and high-speed interfaces, LFE3-95EA-7LFN1156C facilitates efficient implementation of complex image processing pipelines.

- The flexibility of FPGA-based solutions allows for seamless integration with existing hardware platforms and software frameworks.

- By offloading computational tasks from traditional processors, LFE3-95EA-7LFN1156C enhances overall system performance and energy efficiency.

Considerations:

- While LFE3-95EA-7LFN1156C offers unparalleled flexibility, designing efficient FPGA-based systems requires expertise in hardware design, algorithm optimization, and system integration.

- Careful resource management and optimization are crucial to maximize the utilization of FPGA resources and minimize power consumption.

- Continuous validation and testing are essential to ensure the accuracy and reliability of the object recognition system across diverse environmental conditions.

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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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