Technical Name RecAccel™ N3000 PCIe
Project Operator NEUCHIPS
Project Host Dr. Youn-Long Lin
Driven by Deep Understanding of Cloud Recommendation
The revolutionized domain-specific architecture design for cloud recommendation is a result of deep insights into the intricate interplay between compute-bound, latency-bound, memory-bound, energy-bound, and accuracy-bound requirements. With supreme algorithmic optimizations, hardware acceleration, data caching, and power management, this design promises to deliver unparalleled performance and efficiency, setting a new standard for cloud recommendation systems.
Scientific Breakthrough
Industry Leading Results for MLPerf™ DLRM Inference Benchmarking The RecAccel™ N3000 has proven itself to be a leader in both performance and power efficiency in the industry. During MLPerf™ v3.0, it has shown that the RecAccel™ N3000 system delivers 1.7 times better performance per watt for inference DLRM while maintaining 99.9% accuracy with the help of its proprietary INT8 calibrator. SW-HW Co-design Achieves Linear Multicard Scalability During system testing in MLPerf™ v3.0, the RecAccel™ N3000 delivered exceptional performance, with nearly 100% scaling observed across each card. This means that the card's performance increased linearly with the addition of more cards, allowing for seamless scalability and enhanced performance.
Industrial Applicability
Partnership with Taiwan Web Service (TWS) to synergize local eco-system and accelerate AI evolution.
Keyword Semiconductor Application
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  • Satine Chen
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