PRECIOUS: Approximate Real-Time Computing in MLC-MRAM based Heterogeneous CMPs

Enhancing quality of service (QoS) in approximate-computing (AC) based real-time systems, without violating power limits is becoming increasingly challenging due to contradictory constraints, i.e., power consumption and time criticality, as multicore computing platforms are becoming heterogeneous. To fulfill these constraints and optimise system QoS, AC tasks should be judiciously mapped on such platforms. However, prior approaches rarely considered the problem of AC task deployment on heterogeneous platforms. Moreover, the majority of prior approaches typically neglect the runtime architectural phenomena, which can be accounted for along with the approximation tolerance of the applications to enhance the QoS. We present PRECIOUS, a novel hybrid offline-online approach that first schedules AC real-time tasks on a heterogeneous multicore with an objective to maximise QoS and determines the appropriate cluster for each task constrained by a system-wide power limit, deadline, and task-dependency. At runtime, PRECIOUS introduces novel architectural techniques for the AC tasks, where tasks are executed on a heterogeneous platform equipped with multilevel-cell (MLC)-MRAM based last-level cache to improve energy efficiency and performance by prudentially leveraging storage density of MLC-MRAM while ameliorating associated high write latency and write energy. Our novel block management for the MLC-MRAM cache further improves performance of the system, which we exploit opportunistically to enhance system QoS, and turn off processor cores during the dynamically generated slacks. PRECIOUS-Offline achieves up to 76% QoS for a specific task-set, surpassing prior art, whereas PRECIOUS-Online enhances QoS by 9.0% by reducing cache miss-rate by 19% on a 64-core heterogeneous system without incurring any energy overhead over a conventional MRAM based cache design.