Memory energy consumption analyzer for video encoder hardware architectures

Amaral, Livia; Povala, Guilherme; Porto, Marcelo; Silveira, Dieison; Bampi, Sérgio

doi:10.1109/icecs.2016.7841203

Cited by 1 publication

(1 citation statement)

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“…This section presents the energy consumption model developed to calculate the total energy consumption savings resulting from the dynamic management system. The energy consumption model used in this work follows the model presented by Amaral et al [34]. This model can calculate the total energy consumption of the video encoding systems with different configurations, such as data reuse strategies, memory hierarchies, and reference frame compressors.…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Low-energy motion estimation memory system with dynamic management

Silveira

Amaral

Povala

et al. 2021

J Real-Time Image Proc

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The digital video coding process imposes severe pressure on memory traffic, leading to considerable power consumption related to frequent DRAM accesses. External off-chip memory demand needs to be minimized by clever architecture/ algorithm co-design, thus saving energy and extending battery lifetime during video encoding. To exploit temporal redundancies among neighboring frames, the motion estimation (ME) algorithm searches for good matching between the current block and blocks within reference frames stored in external memory. To save energy during ME, this work performs memory accesses distribution analysis of the test zone search (TZS) ME algorithm and, based on this analysis, proposes both a multi-sector scratchpad memory design and dynamic management for the TZS memory access. Our dynamic memory management, called neighbor management, reduces both static consumption-by employing sectorlevel power gating-and dynamic consumption-by reducing the number of accesses for ME execution. Additionally, our dynamic management was integrated with two previously proposed solutions: a hardware reference frame compressor and the Level C data reuse scheme (using a scratchpad memory). This system achieves a memory energy consumption savings of 99.8% and, when compared to the baseline solution composed of a reference frame compressor and data reuse scheme, the memory energy consumption was reduced by 44.1% at a cost of just 0.35% loss in coding efficiency, on average. When compared with related works, our system presents better memory bandwidth/energy savings and coding efficiency results.

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Section: Energy Consumption Modelmentioning

confidence: 99%