arXiv:2606.03026v1 Announce Type: cross Abstract: Spiking language models expose activation sparsity that dense Transformer runtimes do not directly exploit. This paper studies that property from a systems perspective. Building on the SymbolicLight V1 spike-gated language model family, we implement a C++ CPU inference runtime that treats sparse binary spike states as an execution primitive rather than only applying post-hoc weight compression. The runtime combines a manifest-driven weight loader, mixed row/column memory layout, AVX2/FMA kernels, per-channel symmetric INT8 quantization, and int
The increasing scale of AI models necessitates more efficient inference solutions, and this work addresses the energy and computational demands of large spiking language models.
This development indicates a significant step towards more energy-efficient and cost-effective AI deployments, making advanced AI more accessible and sustainable on commodity hardware.
The focus shifts from general Transformer optimization to specialized runtimes that exploit the unique sparsity patterns of spiking neural networks, particularly in language models, enabling practical INT8 inference on CPUs.
- · AI developers
- · Cloud providers
- · Hardware manufacturers (non-GPU)
- · Edge AI applications
- · GPU-centric AI inference solutions (for certain tasks)
- · Less optimized AI inference runtimes
Reduced operational costs and energy consumption for running large language models, especially spiking variants.
Accelerated adoption of spiking neural networks in practical applications due to improved inference efficiency on commodity hardware.
Increased competition in AI inference hardware and software, potentially leading to more specialized AI accelerators beyond traditional GPUs.
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Read at arXiv cs.LG