Technology

Shiitake-powered computer demonstrated by researchers — mushroom-infused chips a surprising alternative to using rare earths in memristors

Source

Tom's Hardware

Published

Oct 26, 2025

TL;DR

AI Generated

Researchers from Ohio State University have demonstrated a unique approach to neuromorphic computing by utilizing shiitake mushroom-infused chips as memristors, offering a sustainable alternative to rare earth materials. The fungal computing via mycelial networks is not only cost-effective but also shows resistance to dehydration and radiation. Memristors are praised for their brain-like efficiency and adaptability, making them suitable for applications like robots and autonomous vehicles. The low power consumption and integration of memory and processing capabilities make shiitake mycelium memristors a promising option for edge computing, aerospace, and embedded firmware applications, bridging bioelectronics and unconventional computing in a sustainable manner.

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New 3D device computes using living brain cells — bioelectronic device uses 3D electronic mesh design paired with living tissue

Researchers at Princeton University have developed a 3D bioelectronic device that merges living brain cells with advanced embedded electronics to create a neural network capable of computational tasks. The device features a 3D mesh design with living tissue that can differentiate patterns using computational techniques. Unlike previous methods using brain cells for computation, this device allows for recording and stimulating neurons' electrical activity at a finer scale. The team successfully trained the device to identify recurring pulse patterns and differentiate between distinct patterns, aiming to scale it for more complex tasks. This technology not only delves into brain computing secrets but also holds potential for understanding and treating neurological diseases while addressing AI's energy consumption challenges.

Tom's Hardware•

1 week ago

SemiEngineering

Research Bits: Apr. 6

Researchers from various universities have made significant advancements in memristor technology. At Loughborough University, a memristor reservoir computing chip was developed, showing improved energy efficiency compared to software-based solutions. At the University of Michigan, a memristor made from 2D bismuth selenide demonstrated long-term data retention and analog tuning capabilities. Additionally, researchers from the University of Cambridge, Beijing Institute of Technology, and Lund University created a highly stable, low-energy hafnium oxide memristor. These developments pave the way for more efficient and versatile AI applications in the future.

SemiEngineering•

3 weeks ago

New Cambridge human brain-inspired chip could slash AI energy use — new type of memristor has roughly a million times lower switching current than conventional devices

A new hafnium oxide memristor developed by researchers at the University of Cambridge operates at switching currents a million times lower than conventional devices, potentially reducing AI energy consumption significantly. The device, engineered by Dr. Babak Bakhit's team, can smoothly switch states at currents below 10 nanoamps, offering hundreds of distinct conductance levels. Memristors like these could lead to neuromorphic systems that cut computing power consumption by over 70%, eliminating the need for energy-intensive data transfers between memory and processing units. Despite promising advancements, a challenge lies in lowering the fabrication temperature to align with standard industry processes, as the current 700°C requirement exceeds CMOS manufacturing tolerances.

Tom's Hardware•

4 weeks ago

Neuromorphic Computing Platform In Perovskite Nickelates (UCSD, Rutgers)

Researchers from UCSD and Rutgers University have published a technical paper introducing a neuromorphic computing platform using perovskite nickelates. This platform combines nonlinear spatiotemporal processing and programmable memory within a single material system. By engineering symmetric and asymmetric hydrogenated NdNiO3 junction devices, the platform achieves ultrafast proton-mediated transient dynamics and stable multilevel resistance states. Networks of symmetric NdNiO3 junctions demonstrate emergent spatial interactions mediated by proton redistribution, enabling real-time pattern recognition and high accuracy in tasks like spoken digit classification and early seizure detection. The platform is described as compact, energy-efficient, and CMOS-compatible, offering integrated processing and memory capabilities for scalable intelligent hardware.