| Specification of HW-V4-ML423-UNI-G-J | |
|---|---|
| Status | Obsolete |
| Series | Virtex?-4 FX |
| Package | Box |
| Supplier | AMD |
| Type | FPGA |
| For Use With/Related Products | XC4VFX100 |
| Platform | Virtex-4 FPGA ML423 UNI Japan |
| Contents | Board(s), Cable(s) – Power Supply Not Included – |
Applications
The HW-V4-ML423-UNI-G-J is designed for integration into high-performance computing environments, particularly in data centers and cloud computing solutions. It supports large-scale machine learning models and deep neural networks, making it ideal for applications such as predictive analytics, natural language processing, and image recognition.
In the financial sector, it can be used for fraud detection systems that require real-time analysis of transactions. Its robust performance ensures minimal latency, crucial for maintaining security protocols. The device operates within a temperature range of -20¡ãC to +60¡ãC, ensuring reliability across various climates.
Key Advantages
1. High computational power with up to 8 teraflops of floating-point operations per second.
2. Scalable architecture allowing for easy expansion from single nodes to clusters.
3. Energy consumption optimized at just 150 watts under maximum load.
4. Certified to meet international safety and environmental standards including CE, FCC, and RoHS.
Frequently Asked Questions
Q1: Can the HW-V4-ML423-UNI-G-J handle complex neural network architectures?
A1: Yes, its high computational capacity and scalable design make it suitable for handling complex neural network architectures efficiently.
Q2: Is there a specific hardware requirement for this model to function optimally?
A2: The optimal performance requires a minimum of 16GB of DDR4 memory and a dedicated cooling system to maintain operational temperatures.
Q3: In which industries is the HW-V4-ML423-UNI-G-J most commonly used?
A3: This model is predominantly used in sectors like finance, healthcare, and telecommunications for tasks requiring advanced machine learning capabilities.
Other people’s search terms
– High-performance computing solution
– Machine learning accelerator card
– Scalable AI processing unit
– Energy-efficient computing module
– Cloud computing hardware for ML
