KAIST researchers stacked fast and slow ion-gated carbon nanotube transistors so a single device remembers both the latest movement and earlier ones. We explain the physics, the reservoir computing idea behind it, what the Advanced Materials paper shows and what it will take to reach a smartwatch.
NUS researchers built a molybdenum disulfide transistor that can act as a logic filter, a neuron or a synapse, and used it to block irrelevant visual data before a spiking neural network processes it. In simulations, cropping digits cut active hardware tiles by about 36% for a 0.3-point accuracy loss, and a traffic scene used about 59% less energy for vehicle recognition and 77% less for licence plates. We explain how it works, check the arithmetic and list what the press release leaves out.
On 18 September 2026 Nature Reviews Physics published a 165-reference Perspective arguing that self-organising memristive networks can learn physically, with no separation between the model and the hardware running it. Six researchers from Los Alamos, INRiM Turin, UCLA, Politecnico di Torino, the University of Canterbury and the University of Sydney set out what has been measured: 93.4% on MNIST learned online on a nanowire device, reservoir computing that trains only a readout layer, and criticality as a tuning target. This piece separates the measured results from the ambition, sets the energy argument against the IEA’s data-centre projections, and lists the four open problems — variability, drift, scaling and readout — that stand between a review paper and a component you can buy.
Discover how ferroelectric memory generative AI enables revolutionary one-chip solutions for true randomness and computing. Learn about FRAM, TRNG, and edge AI breakthroughs that will transform the fu
A practical enterprise guide to neuromorphic processing architectures, GPU energy pressure, cooling constraints, sparse AI workloads, and realistic adoption paths.
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