# Transformer^-1: Input-Adaptive Computation for Resource-Constrained Deployment

arXiv: https://arxiv.org/abs/2501.16394 · HTML: https://mlsanity.com/paper/2501.16394/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2025.

## Abstract (arXiv)

Addressing the resource waste caused by fixed computation paradigms in deep learning models under dynamic scenarios, this paper proposes a Transformer-1 architecture based on the principle of deep adaptivity. This architecture achieves dynamic matching between input features and computational resources by establishing a joint optimization model for complexity and computation. Our core contributions include: (1) designing a two-layer control mechanism, composed of a complexity predictor and a reinforcement learning policy network, enabling end-to-end optimization of computation paths; (2) deriving a lower bound theory for dynamic computation, proving the system's theoretical reach to optimal efficiency; and (3) proposing a layer folding technique and a CUDA Graph pre-compilation scheme, overcoming the engineering bottlenecks of dynamic architectures. In the ImageNet-1K benchmark test, our method reduces FLOPs by 42.7% and peak memory usage by 34.1% compared to the standard Transformer, while maintaining comparable accuracy (±0.3%). Furthermore, we conducted practical deployment on the Jetson AGX Xavier platform, verifying the effectiveness and practical value of this method in resource-constrained environments. To further validate the generality of the method, we also conducted experiments on several natural language processing tasks and achieved significant improvements in resource efficiency.