Recent IoT devices require extremely low standby power consumption, while a certain performance is needed during the active time, and Coarse-Grained Reconfigurable Arrays (CGRAs) have received attention because of their high energy efficiency. For further reduction of the standby energy consumption of CGRAs, the leakage power for their configuration memory must be reduced. Although the power gating is a common technique, the lost data in flip-flops and memory must be retrieved after the wake-up. Recovering everything requires numerous state transitions and considerable overhead both on its execution time and energy. To address the problem, Non-volatile Cool Mega Array (NVCMA), a CGRA providing non-volatile flip-flops (NVFFs) with spin transfer torque type non-volatile memory (NVM) technology has been developed. However, in general, non-volatile memory technologies have problems with reliability. Some NVFFs are stacked-at-0/1, and cannot store the data in a certain possibility. To improve the chip yield, we propose a mapping algorithm to avoid faulty processing elements of the CGRA caused by the erroneous configuration data. Next, we also propose a method to add an error-correcting code (ECC) mechanism to NVFFs for the configuration and constant memory. The proposed method was applied to NVCMA to evaluate the availability rate and reduction of write time. By using both methods, the average availability ratio of 94.2% was achieved, while the average availability ratio of the nine applications was 0.056% when the probability of failure of the FF was 0.01. The energy for storing data becomes about 2.3 times because of the hardware overhead of ECC but the proposed method can save 8.6% of the writing power on average.

A novel configuration data compression technique for coarse-grained reconfigurable architectures (CGRAs) is proposed. Reducing the size of configuration data of CGRAs shortens the reconfiguration time especially when the communication bandwidth between a CGRA and a host CPU is limited. In addition, it saves energy consumption of configuration cache and controller. The proposed technique is based on a multicast configuration technique called RoMultiC, which reduces the configuration time by multicasting the same data to multiple PEs (Processing Elements) with two bit-maps. Scheduling algorithms for an optimizing the order of multicasting have been proposed. However, the multicasting is possible only if each PE has completely the same configuration. In general, configuration data for CGRAs can be divided into some fields like machine code formats of general perpose CPUs. The proposed scheme confines a part of fields for multicasting so that the possibility of multicasting more PEs can be increased. This paper analyzes algorithms to find a configuration pattern which maximizes the number of multicasted PEs. We implemented the proposed scheme to CMA (Cool Mega Array), a straight forward CGRA as a case study. Experimental results show that the proposed method achieves 40.0% smaller configuration than a previous method for an image processing application at maximum. The exploration of the multicasted grain size reveals the effective grain size for each algorithm. Furthermore, since both a dynamic power consumption of the configuration controller and a configuration time are improved, it achieves 50.1% reduction of the energy consumption for the configuration with a negligible area overhead.