Reinforcement Learning for Hardware-Aware Neural Network Design

Abstract

The design of neural networks for deployment on specialized hardware is a significant bottleneck in the widespread adoption of deep learning. Traditional methods often rely on manual tuning or computationally expensive search algorithms, failing to effectively account for the unique constraints and opportunities presented by different hardware platforms. This research proposes a novel approach utilizing reinforcement learning (RL) to automate the process of hardware-aware neural network design. The system learns to optimize network architectures directly by interacting with a simulated hardware environment, receiving rewards based on the network's performance on that specific hardware. The core of the method involves an RL agent that explores the design space of neural networks – considering factors like layer types, number of layers, and connection topologies – guided by performance metrics such as latency and power consumption. This work demonstrates the feasibility and effectiveness of using RL for this complex optimization problem, offering a potentially transformative solution for bridging the gap between neural network design and hardware execution. The key contributions include a framework for modeling hardware constraints within an RL environment and a methodology for learning optimal network architectures that are specifically tailored to the target hardware.