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

TL;DR

This paper introduces Transformer^-1, an adaptive architecture that dynamically allocates computational resources based on input complexity, significantly reducing FLOPs and memory usage while maintaining accuracy, suitable for resource-constrained deployment.

Contribution

It presents a novel input-adaptive Transformer architecture with a control mechanism, theoretical efficiency bounds, and engineering solutions, enabling efficient deployment in resource-limited environments.

Findings

42.7% reduction in FLOPs on ImageNet-1K

34.1% decrease in peak memory usage

Maintains accuracy within ±0.3% of standard Transformer

Abstract

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 ($\pm$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.