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All IPs > Processor > AI Processor > RecAccel N3000 PCIe for AI Recommendation System

RecAccel N3000 PCIe for AI Recommendation System

From NEUCHIPS Corporation

Description

The RecAccel N3000 PCIe card is an advanced AI recommendation solution, designed to deliver unparalleled performance in data inference. This state-of-the-art hardware is tailored for high-demand elastic data centers, offering exceptional reliability and acceleration capabilities for AI-based recommendation systems. The card leverages NEUCHIPS' proprietary RecAccel series AI chips to optimize AI tasks with impressive speed and efficiency.

One of the standout features of the RecAccel N3000 is its deep integration with cloud recommendation architectures. The card boasts supreme algorithmic optimizations, data caching, and dynamic power management to meet the complex needs of compute-bound, latency-bound, memory-bound, and energy-bound applications. By advancing these functionalities, the N3000 ensures seamless scalability, enhancing performance linearly with the addition of extra cards.

A key achievement of RecAccel N3000 is its success in industry benchmarks, particularly its performance in the MLPerf v3.0 DLRM Inference Benchmark. The card achieved an industry-leading performance-per-watt metric, evidencing its efficiency and sustainability in practical applications. Its patented FFP8 technology further enhances recommendation accuracy, achieving 99.99% precision, a crucial advancement for AI recommendation engines.

Features
  • Groundbreaking Perf-per-watt
  • Industry Sustainability Initiatives
  • Exceptional Scalability
Foundries & Process Nodes
Foundry Process Nodes
TSMC 10nm
Tech Specs
Class Value
Categories Processor > AI Processor
Wireline Communication > Ethernet
Processor > Vision Processor
Interface Controller & PHY > AMBA AHB / APB/ AXI
Processor > Coprocessor
Performance-per-Watt Improvement 1.7x
Accuracy 99.99% with FFP8 technology
Availability All Countries & Regions
Applications
  • AI recommendation systems
  • Cloud computing
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