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Although HEVC, the emerging video coding standard, has doubled the coding performance of its predecessor H.264/AVC, its significantly increased computational complexity imposes great obstacles for HEVC encoders to be employed in real-time applications with embedded processors, such as digital signal processors (DSPs). In this paper, a TI Keystone multicore TMS320C6678 DSP-based highly paralleled low-cost fast HEVC encoding solution is well designed and implemented. First, the overall structure of HEVC encoder with CTU-level parallelism is re-designed to well support the encoding parallelism, with full consideration of the hardware characteristics. Second, a low-delay and low-memory multicore data transmission mechanism is proposed to reduce the latency of data access between internal L2 memory and external DDR3. Third, the encoding bottlenecks, i.e., the most time-consuming encoding modules, are identified and optimized for acceleration with TI powerful C6000 SIMD instructions. Experimental results show that our proposed HEVC encoder on TI TMS320C6678 DSPs can significantly improve the real-time capacity with tolerable performance loss, 0.93 dB performance loss under on average 465.50 times speedup as compared to CPU-based HM reference software, more specifically, which makes it desirable in power-constrained real-time video applications. [ABSTRACT FROM AUTHOR]

At 2000 m depth in the oceans, one can hear biological, seismological, meteorological, and anthropogenic activity. Acoustic monitoring of the oceans at a global scale and over long periods of time could bring important information for various sciences. The Argo project monitors the physical properties of the oceans with autonomous floats, some of which are also equipped with a hydrophone. These have a limited transmission bandwidth requiring acoustic data to be processed on board. However, developing signal processing algorithms for these instruments requires one to be an expert in embedded software. To reduce the need of such expertise, we have developed a programming language, called MeLa. The language hides several aspects of embedded software with specialized programming concepts. It uses models to compute energy consumption, processor usage, and data transmission costs early during the development of applications; this helps to choose a strategy of data processing that has a minimum impact on performances. Simulations on a computer allow for verifying the performance of the algorithms before their deployment on the instrument. We have implemented a seismic P wave detection and a blue whales D call detection algorithm with the MeLa language to show its capabilities. These are the first efforts toward multidisciplinary monitoring of the oceans, which can extend beyond acoustic applications. [ABSTRACT FROM AUTHOR]