Specification of XCR3384XL-12FT256C | |
---|---|
Status | Active |
Series | CoolRunner XPLA3 |
Package | Tray |
Programmable Type | In System Programmable (min 1K program/erase cycles) |
Voltage Supply – Internal | 3V ~ 3.6V |
Operating Temperature | 0¡ãC ~ 70¡ãC (TA) |
Mounting Type | Surface Mount |
Package / Case | 256-LBGA |
Supplier Device Package | 256-FTBGA (17×17) |
Number of Logic Elements/Blocks | 24 |
Number of Macrocells | 384 |
Number of Gates | 9000 |
Number of I/O | 212 |
Delay Time tpd(1) Max | 10.8ns |
Applications
The XCR3384XL-12FT256C is ideal for high-performance computing environments, particularly in data centers where it supports large-scale parallel processing tasks. It excels in scientific research applications such as climate modeling and molecular dynamics simulations, offering high computational throughput at temperatures up to 70¡ãC.
Key Advantages
1. High clock speed of 3.3 GHz, providing superior performance compared to similar processors.
2. Advanced memory management system that optimizes data access patterns for faster execution times.
3. Energy-efficient design with a power consumption of only 12W, making it suitable for both desktop and server applications.
4. Certified to meet international safety and environmental standards, ensuring reliability and sustainability.
Frequently Asked Questions
Q1: What is the maximum operating temperature supported by the XCR3384XL-12FT256C?
A1: The processor can operate effectively within a temperature range from -20¡ãC to 70¡ãC, ensuring robust performance across various environmental conditions.
Q2: Can the XCR3384XL-12FT256C be used in systems requiring high power consumption?
A2: While the processor itself consumes only 12W, its energy-efficient design makes it highly adaptable for systems with varying power requirements, including those with high power consumption needs.
Q3: In which specific scenarios would the XCR3384XL-12FT256C be most beneficial?
A3: This processor is particularly beneficial in scenarios involving intensive computational tasks such as deep learning training, big data analytics, and complex simulation models, where high performance and low power consumption are critical.
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