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Japan Unveils Plans for Zettascale Supercomputer: 100 PFLOPs of AI Compute per Node

The zettascale era is officially on the map, as Japan has announced plans to develop a successor to its renowned Fugaku supercomputer. The Ministry of Education, Culture, Sports, Science and Technology (MEXT) has set its sights on creating a machine capable of unprecedented processing power, aiming for 50 ExaFLOPS of peak AI performance with zettascale capabilities. The ambitious "Fugaku Next" project, slated to begin development next year, will be headed by RIKEN, one of Japan's leading research institutions, in collaboration with tech giant Fujitsu. With a target completion date of 2030, the new supercomputer aims to surpass current technological boundaries, potentially becoming the world's fastest once again. MEXT's vision for the "Fugaku Next" includes groundbreaking specifications for each computational node.

The ministry anticipates peak performance of several hundred FP64 TFLOPS for double-precision computations, around 50 FP16 PFLOPS for AI-oriented half-precision calculations, and approximately 100 PFLOPS for AI-oriented 8-bit precision calculations. These figures represent a major leap from Fugaku's current capabilities. The project's initial funding is set at ¥4.2 billion ($29.06 million) for the first year, with total government investment expected to exceed ¥110 billion ($761 million). While the specific architecture remains undecided, MEXT suggests the use of CPUs with special-purpose accelerators or a CPU-GPU combination. The semiconductor node of choice will likely be a 1 nm node or even more advanced nodes available at the time, with advanced packaging also used. The supercomputer will also feature an advanced storage system to handle traditional HPC and AI workloads efficiently. We already have an insight into Monaka, Fujitsu's upcoming CPU design with 150 Armv9 cores. However, Fugaku Next will be powered by the Monaka Next design, which will likely be much more capable.
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Dec 22nd, 2024 01:30 EST change timezone

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