  This paper presents ARCO, an adaptive Multi-Agent Reinforcement Learning
(MARL)-based co-optimizing compilation framework designed to enhance the
efficiency of mapping machine learning (ML) models - such as Deep Neural
Networks (DNNs) - onto diverse hardware platforms. The framework incorporates
three specialized actor-critic agents within MARL, each dedicated to a distinct
aspect of compilation/optimization at an abstract level: one agent focuses on
hardware, while two agents focus on software optimizations. This integration
results in a collaborative hardware/software co-optimization strategy that
improves the precision and speed of DNN deployments. Concentrating on
high-confidence configurations simplifies the search space and delivers
superior performance compared to current optimization methods. The ARCO
framework surpasses existing leading frameworks, achieving a throughput
increase of up to 37.95% while reducing the optimization time by up to 42.2%
across various DNNs.
