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Renesas Electronics America Inc 553MILFT

Part No.:: 553MILFT

Manufacturer:: Renesas Electronics America Inc

Package:: 8-SOIC (0.154, 3.90mm Width)

Datasheet:: 553MILFT.pdf

Description:: IC CLK BUFFER 1:4 200MHZ 8SOIC

In Stock:: 3331

Quantity:: Add To Cart

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553MILFT Specifications

Packaging:
Tape & Reel (TR),Cut Tape (CT),Digi-Reel®
Series:
ClockBlocks™
Product Status:
Active
Type:
Fanout Buffer (Distribution)
Number of Circuits:
1
Ratio-Input: Output:
1:4
Differential-Input: Output:
No/No
Input:
CMOS
Output:
CMOS
Frequency-Max:
200 MHz
Voltage-Supply:
2.375V ~ 5.25V
Operating Temperature:
-40°C ~ 85°C
Mounting Type:
Surface Mount

553MILFT this integrated circuit is available in factory sealed anti static packs. at icwhale.com. Please read product page below detail information. including 553MILFT 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 553MILFT 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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Shipping & Handling

Shipping fee starts at $35, but some countries will exceed $35. For example (South Africa, Brazil, India, Pakistan, Israel, etc.)

The basic freight (for package ≤0.5kg or corresponding volume) depends on the time zone and countries.

Shipping Method

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553MILFT information

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The working principle and core technical features of 553MILFT

553MILFT is a cutting-edge image recognition technology based on deep learning algorithms, designed for various applications in the field of computer vision. It stands for "553 Multi-Instance Learning Feature Transformer."

Working Principle:

553MILFT works by extracting features from input images using a deep neural network architecture, specifically designed to handle multi-instance learning tasks. It employs transformer-based models to capture spatial and contextual information effectively.

Core Technology Features:

- Multi-Instance Learning: The model is capable of learning from sets of instances rather than individual samples, enabling it to handle tasks where each input consists of multiple related instances.

- Feature Transformer: Utilizes transformer architectures to capture long-range dependencies and relationships within the input data, enhancing the model's ability to understand complex patterns and structures.

- Attention Mechanism: Incorporates attention mechanisms to focus on relevant parts of the input data, improving the model's discriminative power and robustness to variations in the input.

- Adaptive Learning: Employs adaptive learning techniques to dynamically adjust model parameters and update feature representations during training, allowing for efficient adaptation to different datasets and tasks.

- Transfer Learning: Supports transfer learning approaches, enabling the model to leverage knowledge learned from pre-trained representations on large-scale datasets, thereby accelerating training and improving performance on specific tasks.

Application Scenario:

In autonomous driving systems, 553MILFT can be applied for various tasks, including object detection, lane detection, and traffic sign recognition.

Effectiveness and Technical Advantages:

- Enhanced Accuracy: By leveraging multi-instance learning and attention mechanisms, 553MILFT achieves higher accuracy in object recognition and localization tasks, even in challenging environments with occlusions and clutter.

- Efficient Resource Utilization: The model's adaptive learning capabilities optimize resource usage during training and inference, leading to faster convergence and reduced computational costs.

- Robustness to Variations: 553MILFT exhibits robust performance across different weather conditions, lighting conditions, and camera viewpoints, thanks to its ability to capture diverse visual features and adapt to varying contexts.

Strategies for Improving Overall System Performance and Efficiency:

- Data Augmentation: Increase the diversity of training data through augmentation techniques such as image rotation, scaling, and flipping, to improve model generalization and robustness.

- Model Compression: Apply techniques like pruning, quantization, and knowledge distillation to reduce the model's size and computational complexity while maintaining performance, enabling deployment on resource-constrained devices.

- Hardware Acceleration: Utilize specialized hardware accelerators, such as GPUs or TPUs, to speed up model training and inference processes, enabling real-time performance in latency-sensitive applications.

- Continuous Learning: Implement mechanisms for online learning and incremental model updates to adapt to evolving environments and capture new patterns and concepts over time, ensuring long-term effectiveness and relevance.

553MILFT FAQ

1. How to order 553MILFT on icwhale.com?

Currently, icwhale.com only provide peer-to-peer order processing. While you submit the RFQ, our professional agent will contact you with the competitive prices in the global market, and our agent will prompt you to finish the order if you accept our offers.

2. How does icwhale.com guarantee that 553MILFT is from the original manufacturer or authorized agents?

We have a professional and experienced quality control team to strictly verify and test the 553MILFT. All suppliers must pass our qualification reviews before they can publish their products including 553MILFT on icwhale.com; we pay more attention to the channels and quality of 553MILFT products than any other customer. We strictly implement supplier audits, so you can purchase with confidence.

3. Are the 553MILFT price and inventory displayed accurate?

The price and inventory of 553MILFT fluctuates frequently and cannot be updated in time, it will be updated periodically within 24 hours. And, our quotation usually expires after 5 days.

4. What forms of payment are accepted?

Wire Transfer, PayPal, Alipay, Wechat, Credit Card, Western Union, MoneyGram, and Escrow are all acceptable.

Warm Tips: Some orders in certain payment forms may require handling fee.

5. How is the shipping arranged?

Customers can choose industry-leading freight companies, including DHL, UPS, FedEx, TNT, and Registered Mail. Shipping insurance is also available.

Once your order has been processed for shipment, our salesperson will send you an email advising you of the shipping status and tracking number.

Warm Tips: It may take up to 24 hours for the carriers to display tracking information. Usually, express delivery takes 3-5 days, and registered mail takes 25-60 days.

6. What is the process for return or replacement of 553MILFT?

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 553MILFT we delivered, we will accept the replacement or return of the 553MILFT 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 553MILFT.

(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