Error-Resilient Data Compression With Tunstall Codes

Abstract

Data compression has been commonly employed to reduce the required memory size for emerging applications with large storage needs like Big Data and Machine Learning (ML). When considering the flexibility of decompression and its hardware implementation, variable-to-fixed length codes (e.g., Tunstall codes) are usually selected. However, memories are prone to suffer different types of errors, causing the stored data to be corrupted; if an error affects the compressed data, it can propagate and cause corruption in a sequence of bits of the decompressed data. Therefore, error resilience should be built-in as part of the memory design to provide reliable data, especially for safety-critical applications. However, Error Correction Codes (ECCs) that are widely used for memory protection, are not very efficient to protect compressed data, because ECCs further increase the memory size and the additional decoding process impacts the latency to decompress the stored data. In this paper, an efficient error-resilient data compression technique with Tunstall codes is proposed; it requires almost no memory overhead and can correct most errors during the decompression process by introducing a conversion table. An enhanced design is also presented to reduce the impact of errors when they cannot be corrected. The proposed scheme has been implemented and evaluated on three ML datasets; results show that it can deal with up to 99.98% errors with almost no memory overhead when Tunstall codes with smaller than 16-bit symbols are employed. The scheme has also been evaluated for two ML applications; results show that even though a small number of errors cannot be corrected in the proposed scheme, they have an extremely low impact on the classification results and the protection overhead is significantly lower than existing ECC techniques.