Embedded - FPGAs (Field Programmable Gate Array) with Microcontrollers

Image	Part Number	Description / PDF	Quantity	Rfq
	EPXA4F672C2 Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA4F672C1 Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA4F672I2 Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA4F672C3 Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA1F672C1ES Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA10F1020C1ES Intel	IC EXCALIBUR ARM 1020FBGA	0
	EPXA1F484C2ES Intel	IC EXCALIBUR ARM 484FBGA	0
	EPXA10F1020C2 Intel	IC EXCALIBUR ARM 1020FBGA	0
	EPXA4F1020C2ES Intel	IC EXCALIBUR ARM 1020FBGA	0
	EPXA4F672C3GZ Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA4F1020C3 Intel	IC EXCALIBUR ARM 1020FBGA	0
	EPXA1F484C1ES Intel	IC EXCALIBUR ARM 484FBGA	0
	EPXA1F672C2ES Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA4F1020C1ES Intel	IC EXCALIBUR ARM 1020FBGA	0
	EPXA4F672C1GZ Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA10F1020C2ES Intel	IC EXCALIBUR ARM 1020FBGA	0
	EPXA1F484C3 Intel	IC EXCALIBUR ARM 484FBGA	0
	EPXA4F672C1ES Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA4F672C2ES Intel	IC EXCALIBUR ARM 672FBGA	0
	EPXA1F484C1 Intel	IC EXCALIBUR ARM 484FBGA	0

Embedded - FPGAs (Field Programmable Gate Array) with Microcontrollers

1. Overview

Embedded Field Programmable Gate Arrays (FPGAs) with microcontrollers represent a hybrid architecture combining programmable logic fabric with hard processor cores. This integration enables parallel processing capabilities of FPGAs with the sequential processing efficiency of microcontrollers. Modern devices often implement ARM Cortex-A/R series cores alongside programmable logic, creating System-on-Chip (SoC) solutions. These ICs are critical in applications requiring real-time processing, hardware acceleration, and flexible I/O interfacing, such as industrial automation, automotive systems, and communication infrastructure.

2. Main Types & Functional Classification

Type	Functional Features	Application Examples
SoC FPGA	Integrated ARM Cortex-A/R cores with programmable logic, advanced memory controllers	Industrial IoT gateways, medical imaging systems
Microcontroller-Embedded FPGA	Low-power microcontroller with small FPGA fabric for custom peripherals	Smart sensors, edge AI devices
AI-Optimized FPGA+MCU	DSP blocks with ML accelerator cores, PCIe/NVMe interfaces	Autonomous vehicle perception systems

3. Structure & Composition

Typical architecture includes:

Programmable Logic Blocks: Logic cells (LUTs, FFs), DSP slices, block RAM
Processing System: Hard ARM cores (Cortex-A53/A72/R5), cache hierarchy, MMU
Interconnect: High-bandwidth AXI buses, NoC (Network-on-Chip) fabric
Memory Interfaces: DDR4/DDR5 SDRAM, HBM2e controllers
Peripherals: Gigabit Ethernet, USB 3.0, CAN FD, PCIe Gen4
Security: Hardware encryption engines (AES-256, SHA-256), secure boot

4. Key Technical Specifications

Parameter	Importance
Logic Density (LUTs): 100K-4M	Determines complexity of implementable functions
Max Clock Frequency: 100MHz-1.5GHz	Impacts processing speed and latency
Power Consumption: 0.5-25W	Crucial for battery-powered/thermal-constrained devices
Memory Bandwidth: 12.8-102GB/s	Limits data-intensive application performance
Interface Speed: 1-64Gbps transceivers	Enables high-speed communication protocols

5. Application Fields

Major industries:

Industrial Automation: Machine vision systems, CNC controllers
Automotive: ADAS sensor fusion units, V2X communication modules
Medical: MRI image processing systems, portable diagnostics
Telecom: 5G massive MIMO beamforming units
Aerospace: Avionics control systems with redundancy

6. Leading Vendors & Products

Vendor	Product Series	Key Features
Xilinx (AMD)	Zynq UltraScale+ MPSoC	Quad Cortex-A53 + GPU + H.264/265 codec
Intel	Stratix 10 SX	4x ARM Cortex-A53 + 28Gbps transceivers
Microchip	PolarFire SoC	RISC-V based Mi-V ecosystem + deterministic latency
Lattice	Avant FPGA	Embedded L2 cache RAM + ML-Accel IP core

7. Selection Recommendations

Key factors to consider:

Processing Requirements: Match core performance with algorithm complexity
Power Budget: Evaluate thermal dissipation requirements
I/O Needs: Verify interface compatibility with peripheral devices
Development Tools: Assess ecosystem maturity (Vivado, Quartus, etc.)
Longevity: Check product lifecycle status for industrial applications

Industry Trend Analysis

Future development directions include:

AI/ML Integration: On-chip neural network accelerators with INT8/BF16 support
Heterogeneous Computing: Combining RISC-V/GPU/FPGA in single die
Quantum-Resistant Security: Post-quantum cryptography implementation
SiP Integration: System-in-Package solutions with 3D-stacked memory
Open-Source Ecosystems: Growth of RISC-V based FPGA platforms