  Deep Reinforcement Learning (DRL) is being increasingly applied to the
problem of resource allocation for emerging System-on-Chip (SoC) applications,
and has shown remarkable promises. In this paper, we introduce SoCRATES (SoC
Resource AdapTivE Scheduler), an extremely efficient DRL-based SoC scheduler
which maps a wide range of hierarchical jobs to heterogeneous resources within
SoC using the Eclectic Interaction Matching (EIM) technique. It is noted that
the majority of SoC resource management approaches have been targeting makespan
minimization with fixed number of jobs in the system. In contrast, SoCRATES
aims at minimizing average latency in a steady-state condition while assigning
tasks in the ready queue to heterogeneous resources (processing elements). We
first show that the latency-minimization-driven SoC applications operate
high-frequency job workload and distributed/parallel job execution. We then
demonstrate SoCRATES successfully addresses the challenge of concurrent
observations caused by the task dependency inherent in the latency minimization
objective. Extensive tests show that SoCRATES outperforms other existing neural
and non-neural schedulers with as high as 38% gain in latency reduction under a
variety of job types and incoming rates. The resulting model is also compact in
size and has very favorable energy consumption behaviors, making it highly
practical for deployment in future SoC systems with built-in neural
accelerator.
