Power-Softmax is a new HE-compatible attention variant that permits training and inference of billion-parameter polynomial LLMs with performance matching standard transformers.
(Leveled) Fully Homomorphic Encryption without Bootstrapping
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Polynomial replacements for activations in MLPs, convolutions, and attention within MetaFormer yield PolyNeXt models that match or exceed standard performance on ImageNet, ADE20K, and robustness benchmarks while beating prior polynomial networks.
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.
citing papers explorer
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Power-Softmax: Towards Secure LLM Inference over Encrypted Data
Power-Softmax is a new HE-compatible attention variant that permits training and inference of billion-parameter polynomial LLMs with performance matching standard transformers.
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Activation-Free Backbones for Image Recognition: Polynomial Alternatives within MetaFormer-Style Vision Models
Polynomial replacements for activations in MLPs, convolutions, and attention within MetaFormer yield PolyNeXt models that match or exceed standard performance on ImageNet, ADE20K, and robustness benchmarks while beating prior polynomial networks.
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AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.