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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.

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This paper presents an integrated and coordinated cross-layer sensing and optimization flow for distributed dark silicon management for tiled heterogeneous manycores under a critical temperature constraint. We target some of the key challenges in dark silicon for manycores, such as: directly focusing on power density/temperature instead of considering simple per-chip power constraints, considering tiled heterogeneous architectures with different types of cores and accelerators, handling the large volumes of raw sensor information, and maintaining scalability. Our solution is separated into three abstraction layers: a sensing layer (involving hardware monitors and pre-processing), a dark silicon layer (that derives thermally-safe mappings and voltage/frequency settings), and an agent layer (used for selecting the parallelism of applications and thread-to-core mapping based on alternatives/constraints from the dark silicon layer).