The encoding/decoding process of JPEG2000 requires much more computation power than that of conventional JPEG mainly due to the complexity of the entropy encoding/decoding. Thus usually multiple entropy codec hardware modules are implemented in parallel to process the entropy encoding/decoding. This module, however, requests many small-size memories to store intermediate data, and when multiple modules are implemented on a chip, employment of the large number of SRAMs increases difficulty of whole chip layout. In this paper, an efficient memory organization framework for the entropy encoding/decoding module is proposed, in which not only existing memory organizations but also our proposed novel memory organization methods are attempted to expand the design space to be explored. As a result, the efficient memory organization for a target process technology can be explored.

A one-dimensional (1-D) full search variable block size motion estimation (VBSME) architecture is presented in this paper. By properly choosing the partial sum of absolute differences (SAD) registers and scheduling the addition operations, the architecture can be implemented with simple control logic and regular workflow. Moreover, only one single-port SRAM is used to store the search area data. The design is realized in TSMC 0.18 µm 1P6M technology with a hardware cost of 67.6K gates. In typical working conditions (1.8 V, 25), a clock frequency of 266 MHz can be achieved.

This paper presents a novel low-energy memory design technique based on variable analysis for on-chip data memory (RAM) in application-specific systems, which called VAbM technique. It targets the exploitation of both data locality and effective data width of variables to reduce energy consumed by data transfer and storage. Variables with higher access frequency and smaller effective data width are assigned into a smaller low-energy memory with fewer bit lines and word lines, placed closer the processor. Under constraints of the number of memory banks, VAbM technique use variable analysis results to perform allocating and assigning on-chip RAM into multiple banks, which have different size with different number of word lines and different number of bit lines tailored to each application requirements. Experimental results with several real embedded applications demonstrate significant energy reduction up to 64.8% over monolithic memory, and 27.7% compared to memory designed by memory banking technique.