| Image | Part Number | Description / PDF | Quantity | Rfq |
|---|---|---|---|---|
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Terasic |
TR10A-HL2 ARRIA 10 GX CARD |
1 |
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Terasic |
FLIK (FPGA CLIENT INNOVATION KIT |
4 |
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Terasic |
TR10A-LP ARRIA 10 GX CARD |
1 |
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Terasic |
TR10A-HL ARRIA 10 GX CARD |
14 |
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Terasic |
DE5A-NET ARRIA 10 GX CARD |
20 |
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Terasic |
BOARD DEV DE5A-NET ARRIA 10GX |
0 |
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Terasic |
DE5A-NET ARRIA 10GX FPGA DEV KIT |
0 |
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Server Acceleration Cards are specialized hardware components designed to enhance computational performance in data centers and enterprise servers. By offloading specific workloads from CPUs, these cards improve processing efficiency for applications like AI training, scientific simulations, and real-time data analytics. Their importance has grown exponentially with the rise of AI-driven workflows and big data processing requirements.
| Type | Functional Characteristics | Application Examples |
|---|---|---|
| GPU Accelerators | Parallel processing with thousands of cores | Deep learning training (e.g., NVIDIA A100) |
| FPGA | Reconfigurable logic for custom workloads | Real-time fraud detection (e.g., Intel Stratix) |
| ASIC Accelerators | Application-specific fixed-function hardware | Cryptocurrency mining (e.g., Bitmain Antminer) |
| SmartNICs | Network packet processing offload | 5G core network virtualization (e.g., Mellanox ConnectX) |
| Storage Accelerators | High-speed NVMe-oF and RAID processing | Distributed storage systems (e.g., Broadcom SmartHBA) |
Typical physical architecture includes: PCIe interface (x16 Gen4/Gen5), processing elements (cores/ALUs), high-bandwidth memory (HBM2/GDDR6), thermal dissipation system (heatsink/fan), and firmware storage. Technical composition involves hardware logic circuits, driver interface, and software acceleration stack (e.g., CUDA/OpenCL).
| Parameter | Description |
|---|---|
| Compute Power (TFLOPS) | Determines maximum processing capability |
| Memory Bandwidth (TB/s) | Impacts data throughput performance |
| Power Consumption (W) | Affects TCO and cooling requirements |
| Interface Speed (PCIe 5.0/CCIX) | Dictates host communication latency |
| Acceleration Algorithms | Supported instruction set specialization |
Primary industries include: Cloud Computing (AWS Inferentia instances), Artificial Intelligence (AlphaFold protein modeling), Financial Services (algorithmic trading), Healthcare (medical imaging analysis), and Autonomous Driving (sensor data processing).
| Vendor | Product Series | Key Features |
|---|---|---|
| NVIDIA | A100/H100 | Tensor Core technology for AI |
| Intel | Habana Gaudi | AI training with RoCE networking |
| AMD | Instinct MI210 | FP64 precision for HPC |
| Xilinx | Alveo U55C | Adaptive compute acceleration |
Consider: workload type (AI vs network vs storage), ecosystem compatibility (existing software stack), power budget, form factor constraints, and long-term maintenance support. For example, choose GPU for general AI workloads but FPGA for ultra-low latency applications.
Future development focuses on heterogeneous integration (CPU+GPU+AI in package), domain-specific architectures (DSA), open-source hardware initiatives (RISC-V based accelerators), and energy-efficient 3D packaging technologies. Market growth is projected at 18.7% CAGR (2023-2030) according to Grand View Research.