CMOS-compatible flash-gated thyristor-based neuromorphic module with small area and low energy consumption for in-memory computing.

ABSTRACT: In-memory computing (IMC) is a technology that enables efficient analog vector-matrix multiplication (VMM). This field has been extensively researched to overcome the performance bottlenecks associated with traditional von Neumann architectures. In addition to analog VMM, combining efficient neuromorphic modules with memory is essential to enable a broader range of IMC operations. Here, we propose a complementary metal-oxide semiconductor (CMOS)-compatible flash-gated thyristor-based neuromorphic module (FGTNM) that combines various functions in neural networks, such as quantization, nonlinear activation, and max pooling into a single module. The FGTNM features a small footprint (53 square micrometers) and low energy consumption (9.1 femtojoules per operation), outperforming previous CMOS-

SUBMITTER: Ko J 

PROVIDER: S-EPMC12273754 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature