An energy characterization platform for memory devices and energy-aware data compression for multilevel-cell flash memory

Abstract

Memory devices often consume more energy than microprocessors in current portable embedded systems, but their energy consumption changes significantly with the type of transaction, data values, and access timing, as well as depending on the total number of transactions. These variabilities mean that an innovative tool and framework are required to characterize modern memory devices running in embedded system architectures. We introduce an energy measurement and characterization platform for memory devices, and demonstrate an application to multilevel-cell (MLC) flash memories, in which we discover significant value-dependent programming energy variations. We introduce an energy-aware data compression method that minimizes the flash programming energy, rather than the size of the compressed data, which is formulated as an entropy coding with unequal bit-pattern costs. Deploying a probabilistic approach, we derive energy-optimal bit-pattern probabilities and expected values of the bit-pattern costs which are applicable to the large amounts of compressed data typically found in multimedia applications. Then we develop an energy-optimal prefix coding that uses integer linear programming, and construct a prefix-code table. From a consideration of Pareto-optimal energy consumption, we can make tradeoffs between data size and programming energy, such as a 41% energy savings for a 52% area overhead.