Improving Error Resilience of Nanoscale Memories [Feature]

Data compression with Huffman codes has been commonly employed to reduce the memory size required for emerging applications with large storage needs, such as Machine Learning (ML). However, memories at the nanoscale can suffer from errors, causing data corruption. This issue is even more severe for memories storing compressed data, because the error can propagate after decompression. This paper proposes an efficient error-resilient data compression scheme applicable to nanoscale memories by employing interleaved data storage and Huffman coding. Based on the evaluation results on protecting two compressed ML models, this scheme reduces the memory overhead by over 99% compared to conventional error protection schemes (such as parity or Hamming codes), while limiting the accuracy loss in the range of 0.26% to 3.27%.