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.
翻译:深度强化学习(DRL)正越来越多地应用于新兴系统芯片(SOC)应用程序的资源分配问题,并展示了令人瞩目的承诺。在本文中,我们引入了SCRATES(SoC Resource AdapTivE 调度器),这是一个以DRL为基础的极为高效的SOC调度器,它利用电相匹配(EIM)技术,将一系列等级性工作映射到苏CL内部的多样化资源中。人们注意到,大部分 SoCRATES资源管理方法一直以系统固定数量的工作岗位为最小化目标。相比之下,SOCRATES的目标是在一个稳定状态条件下尽量减少平均延迟状态,同时为混合资源(处理元素)分配备急列中的任务。我们首先显示,以最小化驱动的 SoC应用程序运行高频工作工作量,并分布/平行工作执行。我们然后展示SOCRATES成功应对了由于在最小化目标中固有的任务依赖性而同时进行观测的挑战。广泛的测试显示,SoCRATES在稳定状态下最大限度地减少平均的悬浮悬浮悬浮悬浮悬浮悬浮悬浮悬浮悬浮悬浮悬浮悬浮悬浮悬浮悬浮状态,同时在38,从而在高额部署中,从而在高额部署类型中,在高额部署中,在高额部署中,在高额部署中,在高额部署中,在不断递升降压压的弹性中取得高位。