MCEA: A Resource-Aware Multicore CGRA Architecture for the Edge

Modern IoT edge devices must address the unpredictability of applications with strict power and temperature constraints. In this scenario, heterogeneous multicore architectures have been driving many solutions due to their high energy efficiency and ability to exploit Task-Level Parallelism. However, while their performance is highly dependent on the quality of the scheduling, their adaptability and generality get restricted when they use fixed-size hardware accelerators. Considering that, this work proposes MCEA, a transparent and power-adaptive multicore reconfigurable architecture. MCEA dynamically adapts the hardware to the workload rather than migrating applications; and predicatively sizes its reconfigurable accelerators without prior knowledge of the applications' behaviors. For that, MCEA uses a synergistic and online profiling system with power gating, achieving performance levels near of homogeneous architectures with fixed and oversized reconfigurable fabric (within 99% on average) while presenting energy efficiency levels similar to heterogeneous architectures statically tuned to a specific workload (within 99% on average). Therefore, MCEA improves Energy-Delay Product in 1.55x and 1.21x when compared to their heterogeneous and homogeneous counterparts, and in 4.72x when compared to a multicore with OoO processors only. We also show that MCEA outperforms a state-of-the-art reconfigurable architecture for the edge under the same power envelope.