CMOS digital-analog mixed signal VLSI implementation of a hippocampus-inspired model

Yuka Shishido, Osamu Nomura, Katsumi Tateno, Hakaru Tamukoh, Takashi Morie

Abstract: For artificial intelligence (AI) to be useful in the home, it is required to acquire unique knowledge of the home obtained through interaction with space and environment. This is difficult for deep learning-based AI. The human brain can learn unique knowledge from few experiences. The entorhinal cortex and hippocampus play essential roles for episodic memory formation and recall. While entorhinal-hippocampal models have been proposed that can reproduce episodic memory, hardware systems that implement such models face challenges related to high computational complexity, power consumption, and processing speed. In this paper, we propose a digital-analog mixed-signal CMOS VLSI implementation of a hippocampus-inspired model that can memorize and associate place and object information essential for the formation of episodic memory. By using both analog and digital in-memory computing architecture, the proposed circuit has achieved a computational efficiency of 22 TOPS/W, which is very high for AI hardware with a learning function. The proposed circuit was fabricated, measured, and evaluated. The results of an experiment using a fabricated chip and a control system showed that the proposed circuit can memorize and process place and object information, and can acquire environment-unique knowledge through interaction with a space.