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Name: MPF200TL-FCG784I
Brand: Microchip Technology
SKU: MPF200TL-FCG784I
Price: 2.40 USD
Availability: InStock
Rating: 5 (2 reviews)

Any Feature, including a modified Existing Feature, that is not an Existing Feature.

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Microchip Technology MPF200TL-FCG784I

Part No.:: MPF200TL-FCG784I

Manufacturer:: Microchip Technology

Package:: 784-BBGA, FCBGA

Datasheet:: MPF200TL-FCG784I.pdf

Description:: IC FPGA 364 I/O 784FCBGA

In Stock:: 2509

Quantity:: Add To Cart

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MPF200TL-FCG784I Specifications

Package/Case:
784-BBGA, FCBGA
Packaging:
Tray
Series:
PolarFire™
ProductStatus:
Active
NumberofLABs/CLBs:
-
NumberofLogicElements/Cells:
192000
TotalRAMBits:
13619200
NumberofI/O:
364
NumberofGates:
-
Voltage-Supply:
0.97V ~ 1.08V
MountingType:
Surface Mount
OperatingTemperature:
-40°C ~ 100°C (TJ)

MPF200TL-FCG784I this integrated circuit is available in factory sealed anti static packs. at icwhale.com. Please read product page below detail information. including MPF200TL-FCG784I 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 MPF200TL-FCG784I 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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MPF200TL-FCG784I information

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MPF200TL-FCG784I application case analysis in emerging technology fields

MPF200TL-FCG784I is a field-programmable gate array (FPGA) with advanced features, ideal for high-performance computing applications. It offers a large number of logic elements, versatile I/O options, and efficient power management capabilities.

Application Scenario:

In the field of artificial intelligence (AI) and machine learning (ML), MPF200TL-FCG784I can be utilized to accelerate complex neural network computations, enabling faster training and inference processes.

Data Processing:

To leverage the capabilities of MPF200TL-FCG784I for AI applications, follow these steps:

1. Neural Network Implementation:

Implement neural network models using frameworks like TensorFlow or PyTorch, optimized for FPGA deployment. Map the neural network layers onto the FPGA's logic elements to parallelize computations and maximize throughput.

2. Input Data Interface:

Design efficient interfaces to stream input data into the FPGA for processing. Utilize high-speed serial interfaces like PCIe or Ethernet to handle large volumes of data from sensors or storage devices.

3. Accelerated Computation:

Offload computationally intensive tasks, such as matrix multiplications and convolutions, to the FPGA for acceleration. Utilize dedicated DSP blocks and optimized algorithms to achieve high-performance computation.

4. Model Optimization:

Optimize neural network models for FPGA deployment by reducing precision requirements and maximizing parallelism. Employ techniques like quantization and pruning to minimize resource utilization while maintaining accuracy.

5. Real-Time Inference:

Deploy the optimized neural network models onto MPF200TL-FCG784I for real-time inference tasks. Utilize the FPGA's reconfigurability to adapt the deployed models to changing inference requirements or input data characteristics.

Considerations:

When utilizing MPF200TL-FCG784I for AI applications, consider the following factors:

- Resource Utilization: Optimize resource utilization to accommodate complex neural network models within the FPGA's constraints.

- Latency and Throughput: Design algorithms and interfaces to minimize inference latency and maximize throughput for real-time AI applications.

- Power Efficiency: Implement power management techniques to maximize the energy efficiency of FPGA-based AI accelerators, especially in edge computing scenarios.

- System Integration: Integrate the FPGA-based AI accelerator with existing hardware and software infrastructure, ensuring seamless interoperability and scalability.

- Performance Validation: Thoroughly validate the performance and accuracy of the FPGA-accelerated neural network models under various operating conditions and datasets.

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3. Are the MPF200TL-FCG784I price and inventory displayed accurate?

The price and inventory of MPF200TL-FCG784I fluctuates frequently and cannot be updated in time, it will be updated periodically within 24 hours. And, our quotation usually expires after 5 days.

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6. What is the process for return or replacement of MPF200TL-FCG784I?

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 MPF200TL-FCG784I we delivered, we will accept the replacement or return of the MPF200TL-FCG784I 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 MPF200TL-FCG784I.

(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