To improve the system performance of multiprocessor system-on-chips (MPSoCs), modern processors have several built-in hardware features, such as prefetchers, which respond to short-term variations in processor load that occurs on a submillisecond scale. However, even the latest dynamic (voltage) frequency scaling governors using reinforcement learning (RL) are implemented in software and, thus, cannot take advantage of these variations. In this work, we propose a hardware RL agent, augmented with preemptive shielding and eligibility traces, to optimize the execution of deadline-bound quality-of-service (QoS) tasks in mixed-critical environments. We demonstrate the features of our algorithm in a hardware-in-the-loop simulation by running LLVM’s single-source benchmarks on SparcV8 processors. We also present our field-programmable gate array (FPGA) implementation with optimized resource usage and timing performance achieved through quantization and approximation.

This paper summarizes the talks of a special session on the IPF 2.0 project, a collaborative German-US research project that leverages self-awareness principles for the self-management of distributed systems of autonomous multiprocessor systems-on-chip (MPSoCs).

Memory approximation techniques are commonly limited in scope, targeting individual levels of the memory hierarchy. Existing approximation techniques for a full memory hierarchy determine optimal configurations at design-time provided a goal and application. Such policies are rigid: they cannot adapt to unknown workloads and must be redesigned for different memory configurations and technologies. We propose SEAMS: the first self-optimizing runtime manager for coordinating configurable approximation knobs across all levels of the memory hierarchy. SEAMS continuously updates and optimizes its approximation management policy throughout runtime for diverse workloads. SEAMS optimizes the approximate memory configuration to minimize energy consumption without compromising the quality threshold specified by application developers. SEAMS can (1) learn a policy at runtime to manage variable application quality of service ( QoS ) constraints, (2) automatically optimize for a target metric within those constraints, and (3) coordinate runtime decisions for interdependent knobs and subsystems. We demonstrate SEAMS’ ability to efficiently provide functions (1)–(3) on a RISC-V Linux platform with approximate memory segments in the on-chip cache and main memory. We demonstrate SEAMS’ ability to save up to 37% energy in the memory subsystem without any design-time overhead. We show SEAMS’ ability to reduce QoS violations by 75% with < 5% additional energy.