Self-Optimizing and Self-Programming Computing Systems: A Combined Compiler, Complex Networks, and Machine Learning Approach

There exists an urgent need for determining the right amount and type of specialization while making a heterogeneous system as programmable and flexible as possible. Therefore, in this paper, we pioneer a self-optimizing and self-programming computing system (SOSPCS) design framework that achieves both programmability and flexibility and exploits computing heterogeneity [e.g., CPUs, GPUs, and hardware accelerators (HWAs)]. First, at compile time, we form a task pool consisting of hybrid tasks with different processing element (PE) affinities according to target applications. Tasks preferred to be executed on GPUs or accelerators are detected from target applications by neural networks. Tasks suitable to run on CPUs are formed by community detection to minimize data movement overhead. Next, a distributed reinforcement learning-based approach is used at runtime to allow agents to map the tasks onto the network-on-chip-based heterogeneous PEs by learning an optimal policy based on Q values in the environment. We have conducted experiments on a heterogeneous platform consisting of CPUs, GPUs, and HWAs with deep learning algorithms such as matrix multiplication, ReLU, and sigmoid functions. We concluded that SOSPCS provides performance improvement up to 4.12\times and energy reduction up to 3.24\times compared to the state-of-the-art approaches.