Category Technology/Electronics

Brain-inspired AI architecture could computing faster and far less power-hungry

New brain-inspired architecture could process data more efficiently
Dual memory pathway abstraction. At the algorithmic level, each layer maintains a shared, low-dimensional state that captures slow contextual dynamics and modulates fast spiking activity. At the hardware level, this separation is mirrored by a heterogeneous accelerator that keeps the compact state on-chip and fuses sparse and dense computations for efficient execution. Credit: Sun et al.

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with each other. While biological neurons exchange information in the form of electrical impulses, SNNs rely on brief signals known as spikes.

SNNs have proved promising for reducing power consumption, as developers can ensure they do not process information continuously, but rather ...

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Soundwaves could power a new kind of chip inspired by the human brain

Neuromorphic functionality and computational benchmarking of TAS. Credit: Science Advances (2026). DOI: 10.1126/sciadv.aec6633

Neuromorphic computing is a computing approach that mimics how the human brain works. Our gray matter is a marvel of nature, capable of handling huge volumes of data with incredible energy efficiency. While modern AI hardware is becoming better at processing complex tasks, it consumes vast amounts of energy.

One of the promises of neuromorphic computing is that it places memory and processing in the same location, using far less energy than traditional AI chips. However, even the most sophisticated neuromorphic systems are fairly simple and don’t come close to matching the number of connections among human neurons.

But a new study published in the journal...

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Could AI tell you where you left your keys?

Could AI tell you where you left your keys?
MIT researchers have developed a long-term memory framework for robots that combines advanced map representations with rich descriptions of the environment. Here, a moving robot attaches detailed descriptions to the bicycles it sees as it explores. Credit: Massachusetts Institute of Technology

An auto factory worker can remember the storage bin where she left a partly assembled component the night before and quickly return to that spot to pick it up. But robots that may work side by side with her would struggle to develop and access this same type of “spatiotemporal” memory.

Now, MIT researchers have developed a long-term memory framework that allows robots to rapidly form and recall a detailed mental model of complicated, large-scale environments...

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Quantum hyperdimensional computing can work 500 times faster than other methods

Quantum computing paradigm inspired by the human brain
Circuit diagram illustrating the two-stage bundling. Credit: npj Unconventional Computing (2026). DOI: 10.1038/s44335-026-00064-6

Cleveland Clinic researchers are unlocking quantum computing’s full potential through the creation of a new computing paradigm inspired by the human brain. Fabio Cumbo, Ph.D., research associate in the lab of Daniel Blankenberg, Ph.D., associate staff, Computational Life Sciences, is developing the model, called quantum hyperdimensional computing (QHDC).

Cumbo published the first-ever implementation of QHDC in two distinct experiments in npj Unconventional Computing.

Hyperdimensional computing (HDC) is a type of computing based in neuroscience. It follows the idea that a concept in the brain is not stored on one single neuron...

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