Massively Parallel Processor Arrays (MPPAs) can be nicely used in portable devices such as tablets and smartphones. However, applications running on mobile platforms require a certain performance level or quality (e.g., high-resolution image processing) that need to be satisfied while adhering to a certain power budget and temperature threshold. As a solution to the aforementioned challenges, we consider a resource-aware computing paradigm to exploit runtime adaptation without violating any thermal and/or power constraint in a programmable MPPA. For estimating the power consumption, we developed a mathematical model based on the post-synthesis implementation of an MPPA in different CMOS technologies while the temperature variation was emulated. We showcase our hardware/software mechanism to load new, on-the-fly configurations into the accelerator, considering quality/throughput tradeoffs for image processing applications. The results show that the average power consumption of a Sobel and Laplace operators using different number of processing elements amounts to 1.24 mW and 10.35 mW, respectively. Furthermore, only 1.64 µs are necessary for configuring a class of MPPA running at 550 MHz.