Providing embedded artificial intelligence with a capacity for palimpsest memory storage Posted on : Jul 19 - 2022

Biological synapses are known to store multiple memories on top of each other at different time scales, much like representations of the early techniques of manuscript writing known as "palimpsest," where annotations can be superimposed alongside traces of earlier writing.

Biological palimpsest consolidation occurs via hidden biochemical processes that govern synaptic efficacy at varying lifetimes. The arrangement can facilitate idle memories to be overwritten without forgetting them, while using previously unseen memories short-term. Embedded artificial intelligence can significantly benefit from such functionality; however, the hardware has yet to be demonstrated in practice.

In a new report, now published in Science Advances, Christos Giotis and a team of scientists in Electronics and Computer Science at the University of Southampton and the University of Edinburgh, U.K., showed how the intrinsic properties of metal-oxide volatile memristors mimicked the process of biological palimpsest consolidation.

Memristors are devices that can regulate the flow of electric current in a circuit to remember the charge flowing through it. Without implementing special instructions, the experimental memristor synapses displayed expanded doubled capacity while protecting a consolidated memory, as up to hundreds of uncorrelated short-term memories temporarily overwrote it. The outcomes highlighted how the emerging memory technologies can effectively expand the capacity of artificial intelligence hardware to conceptualize learning memory.

Biological intelligence vs. artificial intelligence (AI)

Neural networks in the cerebral cortex employ an estimated 1013 to 1014 synapses to generate a range of cognitive abilities, their re-engineered counterparts require an equal number of trainable parameters for far narrower applications.

To explain this learning capacity difference between biological intelligence vs. artificial intelligence, AI researchers suggest that synapses can consolidate multiple memories to be revealed at different time scales, much like a palimpsest. While synapses can remember long-term plasticity events, they can express altered states in the short-term. As a result, the brain can use the same resource for a range of computational processes. This flexibility can offer neuromorphic hardware a major milestone to integrate AI across a broad range of on-the-edge, continuously on learning systems.

During the experiments, researchers had previously designed synapses that are largely based on phase change memory materials, and resistive random-access memory (RRAM)-based memristors to implement metaplasticity to tune the learning rate of artificial synapses in neural networks.

Giotis and the team built this study based on previous work, to bridge the synaptic plasticity with automatic consolidation and memory protection of learned memories from synaptic modifications—a vital element for efficient online learning. The team explored the characteristics of RRAM volatility to mimic the hidden biochemical processes facilitating palimpsest consolidation in biological intelligence. View more