Custom Algorithm-based Fault Tolerance for Attention Layers in Transformers

2025-07-22

AI Summary

Existing hardware accelerators for Transformers suffer from high detection overhead and low fault coverage for random errors in attention mechanisms—particularly during softmax normalization—due to the numerical instability and nonlinearity of softmax. To address this, we propose Flash-ABFT: an end-to-end, single-pass online checksum-based algorithmic fault-tolerance method. Flash-ABFT unifies numerical constraints across Q/K/V matrix multiplications and softmax computation, circumventing the breakdown of conventional Algorithm-Based Fault Tolerance (ABFT) at the softmax stage and eliminating redundant multi-stage checksums. By tightly integrating checksum computation into the forward inference pipeline, it achieves high-accuracy error detection with only 5.3% area and <1.9% energy overhead. To our knowledge, Flash-ABFT is the first solution enabling lightweight, robust, and formally verifiable fault tolerance for the entire attention layer—establishing a new reliability paradigm for large language model (LLM) accelerators.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: Safety and Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Search and Retrieval-Augmented AI: Agentic search
• Security and Privacy: Large-scale security measurements

Abstract

Transformers and large language models (LLMs), powered by the attention mechanism, have transformed numerous AI applications, driving the need for specialized hardware accelerators. A major challenge in these accelerators is efficiently detecting errors caused by random hardware faults. Traditional algorithm-based fault tolerance (ABFT) techniques verify individual matrix multiplications but fall short in handling the full attention mechanism, particularly due to intermediate softmax normalization. This work proposes Flash-ABFT, a novel method that computes an online checksum across the entire three-matrix product of query, key and value matrices, of an attention layer, including the softmax operation, with a single check. This approach significantly reduces overhead by eliminating redundant checks while maintaining high fault-detection accuracy. Experimental results demonstrate that Flash-ABFT incurs only 5.3% hardware area overhead and less than 1.9% energy overhead, making it a cost-effective and robust solution for error detection in attention accelerators.

Problem

• Detect errors in attention layer hardware accelerators
• Handle softmax normalization in fault tolerance checks
• Reduce overhead in fault detection for transformers

Innovation

• Online checksum for three-matrix attention product
• Single-check coverage including softmax operation
• Low overhead with 5.3% area cost