Towards cognitive reconfigurable hardware: Self-aware learning in RTR fault-tolerant SoCs

Abstract

Traditional embedded systems are evolving into power-and-performance-domain self-aware intelligent systems in order to overcome complexity and uncertainty. Without human control, they need to keep operative states in applications such as drone-based delivery or robotic space landing. Nowadays, the partial and run-time reconfiguration (RTR) of FPGA-based Systems-on-chip (SoC) can enable dynamic hardware acceleration or self-healing structures, but this conversely increases system-memory traffic. This paper introduces the basis of cognitive reconfigurable hardware and presents the design of an FPGA-based RTR SoC that becomes conscious of its monitored hardware and learns to make decisions that maintain a desired system performance, particularly when triggering hardware acceleration and dynamic fault-tolerant (FT) schemes on RTR cores. Self-awareness is achieved by evaluating monitored metrics in critical AXI-cores, supported by hardware performance counters. We suggest a reinforcement-learning algorithm that helps the system to search out when and which reconfigurable FT-scheme can be triggered. Executing random sequences of an embedded benchmark suite simulates unpredictability and bus traffic. The evaluation shows the effectiveness and implications of our approach.