LAUSR

Mobile devices, such as smartphones and tablets, offer a plethora of media-rich applications such as photograph and video recording and editing, natural user interfaces, and computer vision. Other areas of embedded image systems are characterized by close-to-sensor processing, such as advanced driver assistance systems (ADAS), mobile scanners, and smart devices used in medical and industrial imaging. Such applications demand highest computing capabilities at stringent resource and power budgets as well as hard real-time constraints. Future scaling of computing performance mandates dramatically improving the energy efficiency of image systems. One rapidly rising trend is to use heterogeneous MPSoCs (multiprocessor system-on-chip), consisting of multiple processors, maybe of a different type, as well as accelerators such as digital signal processors (DSPs), embedded graphics processing units (GPUs), field-programmable gate arrays (FPGAs), or dedicated hardware. Another trend is to use new 3D integrated circuit technologies that allow for tighter integration of processor cores, memory, and sensors to reduce communication latency and improve bandwidth, leading to lower energy consumption, see, for example, the work by Dudek et al. [3]. This calls for novel methodologies for designing heterogeneous hardware architectures, and shielding software developers from growing complexity and allowing them to concentrate on algorithm development rather than on low-level implementation details. Thus, in the last years, several model-based design methods [4, 12, 17] and approaches based on domain-specific programming languages for programming heterogeneous image systems and corresponding compilers have been proposed [2]—prominent examples include Halide [13], HIPAcc [10, 14], and Darkroom [8]. These approaches mainly aim at improving productivity as well as optimizing utilization and performance, but hardly consider real-time aspects. Yet, this special issue covers heterogeneous processor architectures such as mentioned above with an emphasis on real-time image processing.