New Memristor Device Challenges the Von Neumann Bottleneck With Ionic Innovation
Arthur T Knackerbracket has processed the following story:
Memory, or the ability to store information in a readily accessible way, is an essential operation in both computers and human brains. However, there are key differences in how they process information. While the human brain performs computations directly on stored data, computers must transfer data between a memory unit and a central processing unit (CPU). This inefficient separation, known as the von Neumann bottleneck, contributes to the rising energy costs of computers.
The von Neumann bottleneck is a fundamental limitation in computer architecture, named after the mathematician and physicist John von Neumann. It arises from the design of the von Neumann architecture, which uses a single bus for both data and instructions to be fetched from memory. This creates a communication bottleneck because the CPU can either retrieve data or instructions at any given time, but not both simultaneously. Consequently, the speed of data processing is constrained by the memory bandwidth, leading to inefficiencies and slower overall system performance. This bottleneck has driven the development of alternative architectures and optimization techniques to improve data throughput and computational speed.
For over 50 years, researchers have been working on the concept of a memristor (memory resistor), an electronic component that can both compute and store data, much like a synapse. Aleksandra Radenovic of the Laboratory of Nanoscale Biology (LBEN) at EPFL's School of Engineering set her sight on something even more ambitious: a functional nanofluidic memristive device that relies on ions, rather than electrons and their oppositely charged counterparts (holes). This approach would mimic the human brain's way of processing information more closely and is therefore more energy-efficient.
Radenovic says, Memristors have already been used to build electronic neural networks, but our goal is to build a nanofluidic neural network that takes advantage of changes in ion concentrations, similar to living organisms."
We have fabricated a new nanofluidic device for memory applications that is significantly more scalable and much more performant than previous attempts," says LBEN postdoctoral researcher Theo Emmerich. This has enabled us, for the very first time, to connect two such artificial synapses', paving the way for the design of brain-inspired liquid hardware." The research has recently been published in Nature Electronics.
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