AMD Alveo UL3422 accelerator provides high-frequency traders an edge in the race to fastest trade execution while lowering barriers to entry
AMD Alveo UL3422 accelerator card
SANTA CLARA, Calif., Oct. 14, 2024 – AMD (NASDAQ: AMD) today announced the AMD Alveo™ UL3422 accelerator card, the latest addition to its record-breaking family of accelerators1 designed for ultra-low latency electronic trading applications. AMD Alveo UL3422 provides trading firms, market makers and financial institutions with a slim form factor accelerator optimized for rack space, cost and designed for a fast path to deployment in a wide range of servers.
The Alveo UL3422 accelerator is powered by an AMD Virtex™ UltraScale+™ FPGA that features a novel transceiver architecture with hardened, optimized network connectivity cores, custom built for high-speed trading. It enables ultra-low latency trade execution, achieving less than 3ns FPGA transceiver latency and breakthrough ‘tick-to-trade’ performance not achievable with standard off-the-shelf FPGAs1.
“Speed is the ultimate advantage in the increasingly competitive world of high-speed trading,” said Yousef Khalilollahi, corporate vice president & general manager, Adaptive Computing Group, AMD. “The Alveo UL3422 card provides a lower-cost entry point while still delivering cutting-edge latency performance, making it accessible to firms of all sizes that want to stay competitive in the ultra-low latency trading space.”
New Slim Form Factor for Cost-Effective Deployment
The Alveo UL3422 accelerator card is packaged in a slim FHHL (full height, half length) form factor designed to fit into a wide range of servers and co-location exchange data centers.
Compared to its predecessor, the Alveo UL3422 accelerator reduces port density, on-board memory, and connectivity options, while still being powered by the same AMD Virtex UltraScale+ VU2P FPGA for ultra-low latency.
As a result, the Alveo UL3422 is half the size with equivalent performance to the existing Alveo UL3524 accelerator card which holds the current STAC-T0 benchmark world record for tick-to-trade performance1. The slim FHHL form factor of the Alveo UL3422 allows financial institutions to cost-effectively optimize compute density and rack-space.
Ecosystem Solutions and Fast Path to Trade
The Alveo UL3422 accelerator card is designed for a fast path to deployment by utilizing available infrastructure ecosystem solutions and reference designs, giving trading developers the edge they need for rapid design closure and time to market.
It is supported by a growing network of ecosystem partner solutions that provide IP and development frameworks to enable the rapid implementation of trading solutions.
- Exegy, a provider of end-to-end, front-office trading solutions, is supporting the AMD Alveo UL3422 card with its Development Framework (nxFramework). nxFramework is a hardware and software development environment designed to efficiently build and maintain ultra-low latency FPGA applications for the financial industry.
- Hypertec, a provider of hardware, cloud, and value-added solutions for the financial services industry, has closely collaborated with AMD. The company’s HF X410R-G6 server is certified to support the Alveo UL3422 accelerator, making it the first 1U server fully optimized for this card.
- Xelera Technologies, a software provider for high-speed network technology and machine learning (ML) applications, collaborated with AMD to help overcome the latency drawback of ML algorithms in high-frequency trading. With Xelera Silva users can take advantage of real-time, ML-based trading decisions while leveraging XGBoost, LightGBM, CatBoost and other advanced models.
The Alveo UL3422 supports traditional FPGA flows using the AMD Vivado™ Design Suite and comes with a suite of reference designs and performance benchmarks that allow FPGA designers to quickly explore key metrics and develop custom trading strategies to specification.
AMD is also providing developers with the open-sourced and community-supported FINN development framework, enabling low-latency AI models to be deployed into high-performance trading systems. FINN uses PyTorch and neural network quantization techniques designed to reduce the size of AI models while maintaining accuracy. The FINN compiler generates Quantized Neural Network (QNN) Hardware IP blocks that can be used with AMD FPGAs.
The AMD Alveo UL3422 accelerator card is currently available and shipping in production volumes to global financial services customers.
