arXiv:2511.21513v2 Announce Type: replace Abstract: Deploying Transformer models on edge devices is limited by latency and energy budgets. While INT8 quantization effectively accelerates the primary matrix multiplications, it exposes the softmax-related path as the dominant bottleneck. This stage incurs a costly dequantize -> softmax -> requantize detour, which can account for up to 65% of total attention latency and disrupts the end-to-end integer dataflow critical for edge hardware efficiency. To address this limitation, we present IntAttention, the first fully integer attention pipeline tha
The proliferation of AI models demands efficient inference at the edge, pushing research into specialized hardware and algorithmic optimizations to overcome power and latency constraints.
This development significantly lowers the barrier for deploying advanced AI on low-power, resource-constrained devices, expanding the reach and utility of AI applications beyond cloud data centers.
Hardware previously limited by complex floating-point operations can now more efficiently run sophisticated Transformer models using integer-only attention pipelines, enabling broader edge AI implementation.
- · Edge AI device manufacturers
- · Semiconductor companies specializing in AI accelerators
- · IoT industry
- · Robotics manufacturers
- · Cloud-centric AI inference providers (for certain use cases)
- · Companies relying solely on traditional floating-point AI acceleration
- · Developers neglecting hardware-aware AI optimization
Increased capability for real-time AI processing directly on industrial, consumer, and autonomous edge devices.
Reduced data transfer overhead and improved privacy due to less reliance on cloud processing for AI tasks.
Acceleration of autonomous systems and smart environments as AI becomes more ubiquitous and energy-efficient at the point of action.
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