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NEUCHIPS Delivers Industry Leading Results for MLPerf v3.0 DLRM Inference Benchmarking

瀏覽次數:6449

TAIPEI, Taiwan - NEUCHIPS, the leader in AI ASIC platforms for deep learning recommendation, participated in MLPerf(TM) v3.0 with their RecAccel(TM) N3000 and demonstrated industry-leading performance and power efficiency. The RecAccel(TM) N3000 system delivered 1.7x better perf-per-watt for inference DLRM while maintaining 99.9% accuracy leveraging its INT8 calibrator.

The RecAccel(TM) Quad-N3000 PCIe card is expected to increase perf-per-watt 2.2x while also delivering the lowest total cost of ownership (TCO). These results give cloud service providers confidence to choose a new solution on existing and future data center platforms.

MLPerf(TM) v3.0 testing was performed on a GIGABYTE G482-Z54 server with AMD EPYC(TM) 7452 32-core CPU and contained eight RecAccel(TM)-N3000-32G-PCIe cards. During the system testing, the RecAccel(TM) N3000 performance resulted in nearly 100% scaling across each card.

"We deliver high-performance computing products that help intelligent systems effectively manage complex data sets," said Kumaran Siva, corporate vice president, Strategic Business Development, AMD. "We are extremely proud of the joint work we have done with NEUCHIPS to achieve leadership performance and power efficiency for DLRM inference in MLPerf(TM) v3.0 using AMD EPYC processors. We look forward to continue working with NEUCHIPS to deliver industry-leading AI solutions."

"We were thrilled to participate in MLPerf(TM) v3.0 and achieve our goal to deliver the world's most energy-efficient DLRM inference platform with the first domain specific architecture," said Youn-Long Lin, the CEO and Chairman of NEUCHIPS. "We look forward to working with the cloud ecosystem to support industry sustainability initiatives."

超大規模資料中心重塑AI基礎設施
特別企劃半導體AI

超大規模資料中心重塑AI基礎設施

為了增加資料傳輸的速度、距離和效率,AI生態系統正在探索射頻(RF)矽中介層技術,以實現互補式金氧半導體(CMOS)、雙極互補式金氧半導體(BiCMOS)與三五族材料的異質整合。

為了增加資料傳輸的速度、距離和效率,AI生態系統正在探索射頻(RF)矽中介層技術,以實現互補式金氧半導體(CMOS)、雙極互補式金氧半導體(BiCMOS)與三五族材料的異質整合。

關鍵字:deep learning