This invention discloses a method for optimizing the topology of a 3D on-
chip network (SoC) for high-speed
data acquisition systems. Addressing the problem that general-purpose 3D SoC architectures are difficult to adapt to the communication characteristics of high-speed
data acquisition systems, leading to
high transmission latency and redundant link resources, this method models the
network topology adjustment process as a Markov
decision process and employs deep
reinforcement learning to achieve
adaptive optimization of the topology. The method uses a general-purpose 3D SoC as the initial architecture, constructing a
state representation that includes node connection features, average network latency, longest path latency, and link area; defining a
pruning and regrowth action space; designing a reward function that integrates changes in average latency, longest path latency, and link area; and using a deep Q-
learning network with a
multilayer perceptron structure for policy training. To improve learning efficiency, an action candidate set is constructed and combined with a two-layer greedy policy to achieve fast and effective search. Through iterative learning and topology updates, a 3D SoC topology that better suits the communication load of high-speed
data acquisition systems can be obtained, reducing average
transmission latency and link area overhead while ensuring
connectivity.