| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
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Xilinx |
EVAL VCK190 VERSAL AI CORE ED |
0 |
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Xilinx |
ZYNQ-7000 ZC702 EVAL KIT |
0 |
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Xilinx |
KIT EVAL RFSOC ARMA53 W/O PWR SP |
0 |
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Xilinx |
XILINX KINTEX ULTRASCALE+ FPGA K |
0 |
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Xilinx |
EVAL KIT ZYNQ-7000 ZC706 |
0 |
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Xilinx |
DEV USCALE+ RFSOC ZCU216 NO PWR |
0 |
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Xilinx |
KIT VIRTEX7 VC709 CONNECTIVITY |
0 |
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Xilinx |
DEV KIT FOR VCU110 |
0 |
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Xilinx |
VIRTEX-7 FPGA VC7203 EVAL KIT |
0 |
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Xilinx |
EVAL VCK190 VERSAL AICORE NO PWR |
0 |
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Xilinx |
KINTEX-7 FPGA KC705 EMB EVAL KIT |
0 |
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Xilinx |
KIT EVAL FPGA KCU105 JAPAN |
0 |
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Xilinx |
EVAL BOARD KIT ZCU111 W/O PWR SP |
0 |
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Xilinx |
KINTEX-7 FPGA KC705 EVAL KIT |
0 |
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Xilinx |
KIT EVAL ARTIX7 AC701 JAPAN |
0 |
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Xilinx |
KIT ZUP RFSOC ZCU1285 |
0 |
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Xilinx |
KINTEX-7 FPGA KC705 CON EVAL KIT |
0 |
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Xilinx |
EVAL BOARD VIRTEX-7 CHARACTER |
0 |
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Xilinx |
EVAL VMK180 VERSAL PRIME ED |
0 |
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Xilinx |
KIT ZUP RFSOC ZCU1285 W/O PWR SP |
0 |
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Evaluation boards for Field-Programmable Gate Arrays (FPGAs) and Complex Programmable Logic Devices (CPLDs) are hardware platforms designed to test and develop embedded systems using reconfigurable logic. These boards enable engineers to prototype custom digital circuits without permanent integrated circuit fabrication. As key tools in modern electronics development, they support rapid iteration for applications ranging from aerospace systems to consumer electronics.
| Type | Functional Features | Application Examples |
|---|---|---|
| FPGA Evaluation Board | High logic density, reconfigurable fabric, DSP blocks, high-speed transceivers | 5G baseband processing, AI accelerators |
| CPLD Development Kit | Lower power consumption, non-volatile configuration, simpler architecture | Industrial motor control, protocol bridging |
| SoC Embedded Board | Combines FPGA fabric with hard processor cores (ARM, RISC-V) | Smart sensors, autonomous vehicle perception systems |
| Prototype Verification Platform | Multi-FPGA systems with clock/data synchronization | ASIC pre-validation, high-energy physics experiments |
Typical evaluation boards include: - FPGA/CPLD chip with BGA packaging - On-board memory (DDR4 SDRAM, QDR SRAM) - Communication interfaces (PCIe Gen4, Gigabit Ethernet, USB 3.2) - Power management circuits with multiple voltage rails - Debug interfaces (JTAG, UART, FTDI chips) - Expansion headers (FMC, PMOD) - Oscillators and clock distribution networks
| Parameter | Description |
|---|---|
| Logic Elements Count | Defines design complexity capacity (e.g., 500K-2M LUTs for high-end FPGAs) |
| Max Clock Frequency | Determines processing speed (up to 1.6GHz for networking applications) |
| I/O Count & Standards | Supports LVDS, SSTL, HSTL (e.g., 1024 differential pairs) |
| Power Consumption | Thermal design power (TDP) range 5W-35W |
| Memory Bandwidth | Up to 256-bit AXI interfaces with 1866MT/s DDR4 |
| Transceiver Speed | 28Gbps/58Gbps serial links for optical communication |
Major industries include: - Telecommunications (5G NR base stations, optical transport) - Industrial Automation (real-time motion control, machine vision) - Automotive (ADAS sensor fusion, autonomous driving) - Medical Imaging (ultrasound beamforming, MRI reconstruction) - Consumer Electronics (AR/VR video processing) - Scientific Research (particle detector data acquisition)
| Manufacturer | Representative Product |
|---|---|
| Xilinx (AMD) | Zynq UltraScale+ MPSoC ZCU106 |
| Intel (Altera) | Stratix 10 GX 10M Evaluation Board |
| Lattice Semiconductor | iCE40 UltraPlus Breakout Board |
| Microchip (Microsemi) | SmartFusion2 SoC FPGA Development Kit |
| Allwinner (China) | Tang Nano 9K FPGA Module |
Key considerations: - Match logic density to design complexity with 20% margin - Verify interface compatibility (e.g., PCIe Gen5 vs Gen4) - Assess power delivery capability for worst-case scenarios - Check toolchain support (Vivado, Quartus, Lattice Diamond) - Consider thermal management requirements - Evaluate available IP core ecosystem - Compare cost/performance ratio for production scalability
Emerging trends include: - AI/ML acceleration with quantized neural networks - Heterogeneous integration (photonics, RF components) - Open-source toolchain development (e.g., Yosys, SymbiFlow) - Energy-efficient architectures for edge computing - Increased adoption in safety-critical systems (ISO 26262 compliance) - Growth of RISC-V based FPGA SoCs - China's domestic substitution initiatives (Huawei HiSilicon, Phytium)