Policy Gradient-Based Core Placement Optimization for Multichip Many-Core Systems.

As many deep neural network models become deeper and more complex, processing devices with stronger computing performance and communication capability are required. Following this trend, the dependence on multichip many-core systems that have high parallelism and reasonable transmission costs is on the rise. In this work, in order to improve routing performance of the system, such as routing runtime and power consumption, we propose a reinforcement learning (RL)- based core placement optimization approach, considering application constraints, such as deadlock caused by multicast paths.