AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
SymbolicLight V1 is presented, a spike-gated dual-path language model that couples binary Leaky Integrate-and-Fire dynamics with a continuous residual stream that shows no clear accuracy separation across five zero-shot benchmarks.
This work constructs a fully TTFS-based SNN architecture and train it end-to-end, and introduces a reference-based strategy specifically to encode the four core LLM components: embedding layers, layer normalization, attention-related operations and dropout.
QuantaSpike is proposed, a short-window spike-driven quantization framework for LLMs built around Logarithmic Ternary Integrate-and-Fire (LTIF) neurons that achieves state-of-the-art or competitive perplexity and zero-shot accuracy among spike-driven LLM quantization methods.
The 194M-parameter model is implemented on an Alveo U50C FPGA using digital fixed-point arithmetic and on an ARM CPU using sparse integer execution to connect event sparsity to omitted computation and data movement in SymbolicLight V2.
URCHIN (Unified Recurrent Connectome with Horizontal Integrate-and-fire Neurons), which applies the Parallelized Hierarchical Connectome Spiking State-space Model (PHCSSM) to language modeling and offers a biologically plausible, efficient, and directly deployable reference point.
SpikeWorld, a 1.45M-parameter sparse spiking model jointly trained for heterogeneous sensory prediction, semantics, image-text binding and action-conditioned dynamics, is introduced, showing that the contribution is not superior linear identification, but its integration with a frozen multimodal spiking checkpoint.
Lapis is proposed, a spiking attention mechanism that scores each token pair by the L1 distance between its query and key first-spike latency vectors under time-to-first-spike coding, and maps this distance to an affinity through a Laplacian kernel.
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