Low-energy motion estimation memory system with dynamic management.

Area of Science:

• Computer Engineering
• Digital Signal Processing
• VLSI Design

Background:

• Digital video coding demands substantial memory traffic, leading to high power consumption from frequent DRAM accesses.
• Minimizing external memory access is crucial for energy efficiency and extended battery life in video encoding.
• Motion Estimation (ME) algorithms, essential for exploiting temporal redundancies, heavily rely on external memory for reference frames.

Purpose of the Study:

• To reduce the memory traffic and power consumption associated with the Test Zone Search (TZS) ME algorithm.
• To propose a novel memory architecture and dynamic management strategy for energy-efficient video encoding.
• To enhance battery lifetime by optimizing memory access patterns during video processing.

Main Methods:

• Analysis of memory access patterns for the TZS ME algorithm.
• Design of a multi-sector scratchpad memory with dynamic management (Neighbor Management).
• Integration of the proposed system with a hardware reference frame compressor and a Level C data reuse scheme.

Main Results:

• The proposed Neighbor Management reduces static and dynamic power consumption through power gating and optimized access reduction.
• Achieved significant memory energy savings of $$ compared to a baseline system.
• Demonstrated a memory energy reduction of $$ over a reference frame compressor and data reuse scheme, with only $$ average loss in coding efficiency.

Conclusions:

• The developed dynamic memory management system effectively reduces memory energy consumption in digital video coding.
• The proposed architecture offers superior memory bandwidth and energy savings compared to existing solutions.
• This approach provides a viable method for enhancing energy efficiency in battery-powered video encoding devices.