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arXiv · 2609.27437

Mamba-Family State-Space Model Kernels on a Programmable CGLA

Takuto Ando·Yasuhiko Nakashima
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Abstract

Edge and embedded inference is constrained by power and data movement. Mamba-family state-space models replace attention with sequence-linear recurrence, but their inference path combines dense projections, short-reduction SSD kernels, and recurrent-state updates. This paper maps these kernel groups onto IMAX, a programmable CPU-Grounded Linear Array (CGLA), and measures them from kernel execution to token-level integration. Projection kernels match the long-reduction IMAX pipeline, whereas SSD Step-1 is limited by short reductions and kernel-boundary overheads. Mamba-130M token-level integration identifies projection GEMV as the decode bottleneck. These results show that programmable CGLAs fit long-reduction projection kernels, while SSD and decode-time projection support require boundary reduction and persistent-weight execution.

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Takuto Ando, Yasuhiko Nakashima. 2026-09-23. Mamba-Family State-Space Model Kernels on a Programmable CGLA. https://arxiv.org/abs/2609.27437

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