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Sangjin Kim

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#generative ai Oct 2026

MIDAS: An Energy-Efficient Microscaling Quantization-Based Digital CIM Accelerator With Spatio-Temporal Cyclic Alignment for Generative AI

Digital compute-in-memory (DCIM) and the microscaling (MX) data format have emerged as promising hardware and algorithmic solutions, respectively, for energy-efficient generative AI (GenAI) processing. However, their combination introduces challenges, including redundant computation from mantissa alignment, excessive e...

Jiwon Choi, Seongyon Hong, Wooyoung Jo et al. · 0 citations
#artificial intelligence Preprint Aug 2026

A.X K2 Technical Report

To support long contexts efficiently, Sparse Gated Attention (SGA), which combines sparse attention with gated attention, and adopt Gated Norm (GN) to stabilize large-scale training is introduced, which keeps 4-bit NVFP4 serving within one point of FP8 accuracy.

Cheolseung Baek, Dhammiko Arya, Eunki Kim et al. · 0 citations

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