Parameters |
Factory Lead Time |
1 Week |
Package / Case |
256-LBGA |
Number of Pins |
256 |
Operating Temperature |
-55°C~125°C TJ |
Packaging |
Tray |
Series |
SmartFusion® |
Part Status |
Active |
Moisture Sensitivity Level (MSL) |
3 (168 Hours) |
HTS Code |
8542.39.00.01 |
Reach Compliance Code |
unknown |
Frequency |
100MHz |
Base Part Number |
A2F500M3G |
Interface |
EBI/EMI, Ethernet, I2C, SPI, UART, USART |
Number of I/O |
MCU - 25, FPGA - 66 |
RAM Size |
64KB |
Core Processor |
ARM® Cortex®-M3 |
Peripherals |
DMA, POR, WDT |
Connectivity |
EBI/EMI, Ethernet, I2C, SPI, UART/USART |
Architecture |
MCU, FPGA |
Core Architecture |
ARM |
Primary Attributes |
ProASIC®3 FPGA, 500K Gates, 11520 D-Flip-Flops |
Flash Size |
512KB |
RoHS Status |
Non-RoHS Compliant |
This SoC is built on ARM? Cortex?-M3 core processor(s).
A ARM? Cortex?-M3 core processor(s) is built into this SoC.According to the manufacturer, this system on a chip has a package of 256-LBGA.With 64KB RAM implemented, this SoC chip provides users with reliable performance.Using the MCU, FPGA technique, this SoC design's internal architecture is simple.The system on a chip is part of the series SmartFusion?.This SoC meaning should have an average operating temperature of -55°C~125°C TJ.This SoC security combines ProASIC?3 FPGA, 500K Gates, 11520 D-Flip-Flops, an important feature to keep in mind.Featured in the state-of-the-art Tray package, this SoC system on a chip provides outstanding performance.MCU - 25, FPGA - 66 I/Os are available in this SoC part.This flash has a size of 512KB.By searching A2F500M3G, you will find system on chips with similar specs and purposes.There is 100MHz frequency associated with the wireless SoC.In this SoC meaning, ARM serves as the core architecture.In this computer SoC, there are 256 pins.
ARM? Cortex?-M3 processor.
64KB RAM.
Built on MCU, FPGA.
512KB extended flash.
Core Architecture: ARM
There are a lot of Microsemi Corporation
A2F500M3G-1FG256M System On Chip (SoC) applications.
- Temperature Sensors
- Efficient hardware for training of neural networks
- Medical Pressure
- Mobile computing
- Deep learning hardware
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- Cyber security for critical applications in the aerospace