Content-assisted file decoding for nonvolatile memories

Nonvolatile memories (NVMs) such as flash memories play a significant role in meeting the data storage requirements of today´s computation activities. The rapid increase of storage density for NVMs however brings reliability issues due to closer alignment of adjacent cells on chip, and more levels that are programmed into a cell. We propose a new method for error correction, which uses the random access capability of NVMs and the redundancy that inherently exists in information content. Although it is theoretically possible to remove the redundancy via data compression, existing source coding algorithms do not remove all of it for efficient computation. We propose a method that can be combined with existing storage solutions for text files, namely content-assisted decoding. Using the statistical properties of words and phrases in the text of a given language, our decoder identifies the location of each subcodeword representing some word in a given input noisy codeword, and flips the bits to compute a most likely word sequence. The decoder can be adapted to work together with traditional ECC decoders to keep the number of errors within the correction capability of traditional decoders. The combined decoding framework is evaluated with a set of benchmark files.