Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising
Autoregressive LLMs are inefficient due to full parameter access per token. Masked diffusion models (MDLMs) improve memory-bound settings. This paper proposes neuromorphic MDLMs (N-MDLMs) combining block diffusion and spike-based computation for further efficiency gains. Block diffusion increases token throughput, while spike sparsity reduces parameter traffic. Experiments on translation tasks show N-MDLMs achieve better energy efficiency and throughput even on compute-bound platforms.
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[Submitted on 24 Jul 2026]
Title:Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising
View a PDF of the paper titled Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising, by Dengyu Wu and 4 other authors
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Abstract:Autoregressive (AR) large language models (LLMs) are inherently inefficient at inference time because each generated token requires accessing the full set of model parameters, leading to low operational intensity and high energy consumption. Masked diffusion language models (MDLMs) partially address this limitation for memory-bound settings by allowing multiple tokens to be generated per parameter access. In order to further enhance inference efficiency on modern platforms with extensive in-chip memory, this work proposes neuromorphic MDLMs (N-MDLMs), which integrate block diffusion with spike-based neuromorphic computation to jointly improve throughput and energy efficiency. While block diffusion increases token throughput by producing multiple tokens per parameter access, spike-induced sparsity reduces effective parameter traffic and computations by skipping inactive channels. To analyze the synergistic effect of sparsity and diffusion, we develop a token-level roofline-inspired model that captures the combined impact of block-parallel generation and spike sparsity on decoding efficiency. Experimental results on translation tasks show that, thanks to spike-induced sparsity, N-MDLMs achieve substantial improvements in energy efficiency and throughput even in compute-bound platforms for which MDLMs would fail to improve over AR-LLMs.
Comments: Accepted for presentation at 2026 IEEE Workshop on Signal Processing Systems (SiPS)
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG); Signal Processing (eess.SP)
Cite as: arXiv:2607.24841 [cs.CL]
(or arXiv:2607.24841v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.24841
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Clement Ruah [view email] [v1] Fri, 24 Jul 2026 11:14:57 UTC (1,444 KB)
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