Server Acceleration Cards

Part Number	Description / PDF	Quantity
A-U200-P64G-PQ-G Xilinx	BOARD DCAB SERVER U200 PASSIVE	11
A-U250-P64G-PQ-G Xilinx	BOARD DCAB SERVER U250 PASSIVE	3
A-U50DD-P00G-ES3-G Xilinx	BOARD DCAB ALVEO U50 NET PASSIVE	10
A-U250-A64G-PQ-G Xilinx	BOARD DCAB SERVER U250 ACTIVE	9
A-U200-A64G-PQ-G Xilinx	BOARD DCAB SERVER U200 ACTIVE	15
A-U50-P00G-PQ-G Xilinx	BOARD DCAB ALVEO U50 NET PASSIVE	35
A-U50-P00G-LV-G Xilinx	BD DCAB ALVEO U50LV NET PASSIVE	2
A-U280-P32G-PQ-G Xilinx	BOARD DCAB SERVER U280 PASSIVE	1
A-U280-A32G-DEV-G Xilinx	BOARD DCAB SERVER U280 ACTIVE	7

Server Acceleration Cards

1. Overview

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.

2. Major Types and Functional Classification

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)

3. Structure and Components

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).

4. Key Technical Specifications

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

5. Application Fields

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).

6. Leading Vendors and Products

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

7. Selection Recommendations

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.

Industry Trends Analysis

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.