Energy-Aware Coarse Grained Reconfigurable Architectures Using Dynamically Reconfigurable Isolation Cells

This thesis presents a self adaptive power management system to improve energy efficiency of coarse-grained reconfigurable architectures (CGRAs). CGRAs can host multiple applications on a single platform. Moreover, a single application may have multiple versions which have different degree of parallelism (fully serial, partially serial, fully parallel etc.). Selection of the optimum application version depends on runtime conditions such as resource availability on the platform. A traditional worst case design to satisfy its specifications results in undesirable power efficiency. Existing solutions to this problem offer costly hardware to mainly employ dynamic voltage and frequency scaling (DVFS). We propose exploiting reconfiguration of available resources on CGRA. Our solution makes use of dynamically reconfigurable isolation cells (DRICs) instead of dedicated hardware. We also introduce autonomous parallelism, voltage and frequency selection (APVFS) to realize DVFS functionality and to select the optimum version. Three applications are used for simulations, namely; matrix multiplication, finite impulse response filter (FIR) and fast Fourier transform (FFT). Results show that up to 72 % and 55 % power and energy can be saved respectively. Synthesis of the fabric shows considerable reduction in area overheads compared to existing designs employing DVFS