Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding.

To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention. Here, a novel Flash-based CIM architecture is proposed to implement large-scale sparse coding, wherein various matrix weight training algorithms are verified. Then, with further optimizations of mapping methods and initialization conditions, the variation-sensitive training (VST) algorithm is designed to enhance the processing efficiency and accuracy of the applications of image reconstructions. Based on the comprehensive characterizations observed when considering the impacts of array variations, the experiment demonstrated that the trained dictionary could successfully reconstruct the images in a 55 nm flash memory array based on the proposed architecture, irrespective of current variations. The results indicate the feasibility of using Flash-based CIM architectures to implement high-precision sparse coding in a wide range of applications.

An Efficient and Robust Partial Differential Equation Solver by Flash-Based Computing in Memory.

Flash memory-based computing-in-memory (CIM) architectures have gained popularity due to their remarkable performance in various computation tasks of data processing, including machine learning, neuron networks, and scientific calculations. Especially in the partial differential equation (PDE) solver that has been widely utilized in scientific calculations, high accuracy, processing speed, and low power consumption are the key requirements. This work proposes a novel flash memory-based PDE solver to implement PDE with high accuracy, low power consumption, and fast iterative convergence. Moreover, considering the increasing current noise in nanoscale devices, we investigate the robustness against the noise in the proposed PDE solver. The results show that the noise tolerance limit of the solver can reach more than five times that of the conventional Jacobi CIM solver. Overall, the proposed flash memory-based PDE solver offers a promising solution for scientific calculations that require high accuracy, low power consumption, and good noise immunity, which could help to develop flash-based general computing.