E-Flash: Energy-Efficient DNN Mapping on NAND Flash Memory with State-Switching Algorithm

Deep neural network (DNN) has been widely adopted in various applications. Ranging from image classification to text generation, Transformer-based models have demonstrated unprecedented performance. However, they suffer from a significant computational complexity and a large memory footprint, leading to memory-bound issues. While previous research on NAND flash-based neural network computation has been performed, these studies often encounter accuracy problems, as analog processing-in memory (PIM) operations typically lead to inaccurate results. Moreover, studies on NAND flash using single-level cell (SLC) are unable to fully leverage the advantages of efficient storage density on the multi-level cell (MLC) memory. We propose an E-Flash hardware architecture with an energy-efficient DNN mapping method. E-Flash introduces a state-switching algorithm to perform data movements between flash memory and host device in an energy-efficient manner. By reallocating the data in triple-level cell (TLC) NAND flash memory, we reduce the energy consumption during the data read operation. Experimental results demonstrate that E-Flash achieves improved energy consumption compared to baseline under significantly small area overhead for quantized BERT and Llama 2 weights by 37.73% and 16.74%, respectively.