SpikeMLLM is the first spike-based MLLM framework that maintains near-lossless performance under aggressive timestep compression and delivers 9x throughput and 25x power efficiency gains via a custom RTL accelerator.
Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, and Yoon Kim
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A 194M-parameter spiking dual-path model trained on 3B Chinese-English tokens achieves held-out PPL 8.88-8.93 at >89% per-element sparsity, trailing GPT-2 201M by 7.7% while showing that LIF temporal integration outperforms simple top-k masking at matched sparsity.
SDLLM is a spike-driven LLM that uses gamma-SQP two-step encoding, bidirectional symmetric quantization, and membrane potential clipping to achieve 7x lower energy consumption and 4.2% higher accuracy than prior spike-based language models.
citing papers explorer
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SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression
SpikeMLLM is the first spike-based MLLM framework that maintains near-lossless performance under aggressive timestep compression and delivers 9x throughput and 25x power efficiency gains via a custom RTL accelerator.
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SymbolicLight V1: Spike-Gated Dual-Path Language Modeling with High Activation Sparsity and Sub-Billion-Scale Pre-Training Evidence
A 194M-parameter spiking dual-path model trained on 3B Chinese-English tokens achieves held-out PPL 8.88-8.93 at >89% per-element sparsity, trailing GPT-2 201M by 7.7% while showing that LIF temporal integration outperforms simple top-k masking at matched sparsity.
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Spike-driven Large Language Model
SDLLM is a spike-driven LLM that uses gamma-SQP two-step encoding, bidirectional symmetric quantization, and membrane potential clipping to achieve 7x lower energy consumption and 4.2% higher accuracy than prior spike-based language models.