Memristor demonstrates use in fully analog hardware-based neural network

"As AI processing demands reach the limits of current CMOS technology, neuromorphic computing—hardware and software that mimic the human brain's structure—can help process information faster and more efficiently. A new memristor made from 2D layers of bismuth selenide combines long-term data retention and analog tuning to enhance AI energy efficiency and processing speed.

The University of Michigan Engineering study is published in ACS Nano.

The (bismuth selenide) memristor demonstrated three technical requirements that no practical memristors had combined up until this point: long-term data retention, analog-style memory states and the ability to operate regulator-free in circuit. In a demonstration, the memristor successfully controlled a balance lever as part of a fully analog, all-hardware reservoir computing network.

"Our work provides a new pathway for making key components for building hardware-based neural networks. The presented memristors can truly work in a way that AI circuit designers will love," said Xiaogan Liang, a professor of mechanical engineering at U-M and corresponding author of the study.

Memristors, devices that adjust electrical resistance based on past current or voltage, enable in-memory computing, an essential component of neuromorphic computing. The ability to store and process information in the same device eliminates the bottleneck in conventional computing where data must constantly shuttle between separate memory and processing units.

The memristor properties needed for hardware-based neural networks are typically at odds with one another. The devices with long-term data retention through non-volatile memory require an external current-regulating device to prevent abrupt switching. On the other hand, those with analog-style memory states, meaning continuous tuning rather than binary switching, suffer from poor data retention."