An in-memory computing circuit with carry-over thermometer coding for a hippocampus-inspired model

The memory-based hippocampus-inspired model (MBHIM) that has been proposed by the authors is a model that reproduces episodic memory suitable for VLSI implementation. While an MBHIM VLSI circuit can achieve high energy efficiency using the in-memory computing architecture with thermometer coding, its computational functionality was limited to addition operation. The use of thermometer coding, though enabling simple VLSI implementation, remains an integration density challenge. In this paper, we propose a memory cell circuit for MBHIM, which supports addition and subtraction using in-memory computing, enabling broader applications, including time-dependent forgetting mechanisms. We also propose a carry-over thermometer coding scheme for efficient value representation, achieving a reduction of 60-90% transistor count compared to conventional approaches. This scheme maintains the advantages of thermometer coding while achieving high integration density, making it particularly effective for memory devices with high write error rates, such as MRAM and ReRAM. The proposed scheme is promising for realizing brain-inspired AI hardware that achieves low power consumption, high computational efficiency, and high integration density.