, July 7, 2020
/PRNewswire/ -- Alchip Technologies, Limited, the high-performance computing ASIC leader, today revealed that it assisted Preferred Networks, Inc. (PFN), the Japanese leader in deep learning technology, with the development of that company's ground-breaking MN-3 supercomputer.
The MN-3, powered by the MN-Core™ deep learning processor, recently won 1st place in the June 2020
Green500 ranking of the world's most energy-efficient supercomputers. Green500 rankings are published semi-annually by the non-profit Top500 rating service.
MN-Core is a deep learning processor jointly developed by PFN and Kobe
University. Alchip's advanced LSI design technology helped achieve the required device performance. MN-Core is at the leading edge of machine learning with an approx. 800mm sq. per die, designed as an advanced power saving 4-die multi-chip package that incorporates Alchip's proprietary die-to-die interface technology. Alchip executed the ASIC's physical design, trial manufacturing and production.
MN-Core is optimized for matrix operations, a type of computation characteristic of deep learning. It achieves world-class energy efficiency of 1 TFLOPS (half-precision) per watt. MN-3 has four this MN-Core processor. To win first place in the Green500 rankings, PFN operated 40 nodes (total 160 MN-Core) that reached 21.11GFLOPS/W.
"Because the MN-Core is a complex, full reticle sized SoC, it creates a number of design challenges. Chief among them is providing heat dissipation for the enormous 500-watt power consumption," explained Johnny Shen
, CEO, Alchip Technologies. "We also developed mechanical package samples at the very early stage to eliminate the reliability concerns of mounting such a large chip. This complicated test design was a particularly challenging, given the project's tight schedule," he explained.
The high- performance computing ASIC is built on TSMC's process technology. Each die hosts 512 processor core engines. It was designed using Alchip's unique clock tree methodology to achieve the required low-power specs.
The four-in-one 6,400~-ball stacked package is arranged on an organic substrate measuring 85x85mm2
. It incorporates Alchip's proprietary D2D (die-to-die) interface technology that provides the bandwidth demand within a small area/power ratio. Alchip provided total chip and package integration and verification.
"Alchip's leading-edge design technology contributed greatly to the development of PFN's MN-3 supercomputer and its deep learning performance, especially through their high-density, low-voltage implementation of the matrix computation units," Yusuke Doi
, Preferred Networks' Vice President of Computing Infrastructure, explained. "We will continue our research and development in deep learning using MN-Core which Alchip supported us to develop."
"As PFN's foundry partner, TSMC is deeply honored to be part of this joint R&D project with PFN and Alchip, and support PFN's hardware strategy to promote the practical application of Japan's
R&D in deep learning technology and related technologies. We look forward to seeing outstanding achievements from the further acceleration of PFN's research in deep learning and related technologies," said Makoto Onodera
, President of TSMC Japan.
For a more information on Alchip Technologies, go to www.alchip.com
. About Alchip
Alchip Technologies, Ltd., headquartered in Taipei, Taiwan
, is a global leading provider of silicon design and production services for system companies developing complex and high-volume ASICs and SoCs. Alchip was founded by semiconductor veterans from Silicon Valley and Japan
in 2003 and provides faster time-to-market and cost-effective solutions for SoC design at mainstream and advanced, including 7nm processes. Customers include global leaders in AI, HPC/supercomputer, mobile phones, entertainment device, networking equipment and other electronic products. Alchip is listed on the Taiwan Stock Exchange (TWSE: 3661).
is the trademarks or the registered trademarks of Preferred Networks, Inc. in Japan
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SOURCE Alchip Technologies Ltd