AERO: Softmax-Only LLMs for Efficient Private Inference
Keywords: Private inference, LLMs, Architectural Optimization, Entropy Regularization
TL;DR: We propose AERO framework that optimizes architecture of LLMs for efficient private inference, achieving up to 4.23$\times$ reduction in communication and 1.94$\times$ reduction in latency overheads.
Abstract: The pervasiveness of proprietary language models has raised privacy concerns for users' sensitive data, emphasizing the need for private inference (PI), where inference is performed directly on encrypted inputs. However, current PI methods face prohibitively higher communication and latency overheads, primarily due to nonlinear operations. In this paper, we present a comprehensive analysis to understand the role of nonlinearities in transformer-based decoder-only language models. We introduce AERO, a four-step architectural optimization framework that refines the existing LLM architecture for efficient PI by systematically removing nonlinearities such as LayerNorm and GELU and reducing FLOPs counts. For the {\em first time}, we propose a Softmax-only architecture with significantly fewer FLOPs tailored for efficient PI. Furthermore, we devise a novel entropy regularization technique to improve the performance of Softmax-only models. AERO achieves up to 4.23$\times$ communication and 1.94$\times$ latency reduction. We validate the effectiveness of AERO by benchmarking it against the state-of-the-art.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 12265
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