Posted on 09/06/2025 9:25:45 PM PDT by E. Pluribus Unum
Experimental device harnesses quantum properties for efficient processing at room temperature.
Engineers are working to design computers capable of handling a difficult class of tasks known as combinatorial optimization problems. These challenges are central to many everyday applications, including telecommunications planning, scheduling, and route optimization for travel.
Current computing technologies face physical limits on how much processing power can be built into a chip, and the energy required to train artificial intelligence models is enormous.
A collaborative team from UCLA and UC Riverside has introduced a new strategy to address these limitations and tackle some of the hardest optimization problems. Instead of representing all information digitally, their system processes data through a network of oscillators — components that shift back and forth at defined frequencies. This architecture, called an Ising machine, excels at parallel computing, enabling many calculations to run at the same time. The solution to the problem is reached when the oscillators fall into synchrony.
In their report published in Physical Review Applied, the researchers described a device that relies on quantum properties connecting electrical activity with vibrations inside a material. Unlike most existing quantum computing approaches, which must be cooled to extremely low temperatures to preserve their quantum state, this device can function at room temperature.
Figure: (upper panels) Scanning-electron-microscope image showing a charge-density-wave device channel in the coupled oscillator circuit. Pseudo-coloring is used for clarity. Circuit schematic of the coupled oscillator circuit. (lower panels) Illustration of solving the max-cut optimization problem, showing the 6 × 6 connected graph, circuit representation of the six coupled oscillators using the weights described in the connectivity matrix, and values of the phase-sensitivity function. Credit: Alexander Balandin
“Our approach is physics-inspired computing, which...
(Excerpt) Read more at scitechdaily.com ...
So we're going full circle back to analog computers.
Analog computers never went completely away for specialized applications.
wow. that’s cool.
How often do the vacuum tubes need to be replaced?
“How often do the vacuum tubes need to be replaced?”
Never, like all Hoover vacuums, they have a removable dust tray. /s
Here is an article on companies currently working on analog computers and associated products:https://quantumzeitgeist.com/7-analog-computing-companies-powering-next-generation-application/
“Analog computing, a method rooted in continuous physical phenomena like electrical voltages or mechanical movement, stands in contrast to the discrete 0s and 1s of digital computing. Historically, tools like slide rules served as rudimentary analog computers, and even water was once employed for complex economic calculations. However, the modern analog revolution is chip-based, with numerous companies delving into its potential, especially in neuromorphic computing. This approach seeks to emulate the human brain's structure and function, using circuits to mimic neurons and synapses, offering a more efficient and parallel processing alternative to traditional digital methods.
Intel
A tech giant with a significant stake in the future of computing, Intel has ventured into neuromorphic computing with its research chip, “Loihi”. This chip emulates the brain's basic computational unit, the neuron, offering efficient information processing. With 128 cores, each containing 1,024 artificial neurons, Loihi boasts over 130,000 neurons that can be interconnected with approximately 130 million synapses. Its adaptive nature makes it suitable for pattern recognition and sensory data processing tasks.
Intel's foray into analog and neuromorphic computing signifies a broader trend in the tech industry, where companies are looking beyond traditional digital computing to address the challenges of modern-day applications. As the demand for real-time data processing, AI, and machine learning continues to grow, innovations like Loihi are set to play a pivotal role in shaping the future of computing.
Aspinity
Aspinity aims to revolutionize power consumption in always-on sensing devices by focusing on analog processing for edge devices. By processing analog sensor data before its digital conversion, Aspinity’s technology can significantly reduce power usage, a boon for battery-operated devices. Their AnalogML family, starting with the AML100, detects and classifies sensor-driven events from raw, analog sensor data, promising significantly lower-power, always-on-edge processing devices. The flagship product from Aspinity is the AML100, which belongs to their AnalogML™ (analog machine learning) family. The AML100 is designed to detect and classify sensor-driven events directly from raw, analog sensor data. This capability empowers developers to create edge processing devices that are significantly more power-efficient. The underlying technology is based on the unique Reconfigurable Analog Modular Processor (RAMP™) platform, which showcases the company's commitment to pushing the boundaries of analog computing.
IBM
A pioneer in many technological realms, IBM has ventured into neuromorphic computing with its TrueNorth chip. This chip emulates the brain's neurons and synapses with significantly less power. It's tailored for various applications, including real-time processing in sensors and mobile devices.
One of their notable contributions is the TrueNorth chip, which emulates the structure and scale of the brain's neurons and synapses but with a significantly lower power consumption. TrueNorth is designed for various applications, including real-time processing in sensors and mobile devices. This chip showcases IBM's commitment to exploring the potential of analog computing principles in modern-day applications.
BrainChip
Specializing in neuromorphic computing, BrainChip has introduced the Akida Neuromorphic System-on-Chip. This chip is designed to provide advanced neural networking capabilities for both edge and enterprise applications, bridging the gap between artificial neural networks and the human brain's functionality. This innovative chip is designed to provide advanced neural networking capabilities, making it particularly suitable for edge and enterprise applications, such as advanced driver assistance systems, drones, and IoT devices. The architecture of the Akida chip allows for low-power and low-latency processing, which is essential for real-time applications.
HPE
With their “Dot Product Engine” project, HPE is exploring neuromorphic computing. This project focuses on hardware that can accelerate deep-learning tasks using analog circuits to perform matrix multiplications, a foundational operation in deep learning.
The Dot Product Engine is a significant leap in analog computing. By leveraging the inherent advantages of analog circuits, HPE aims to revolutionize the way deep learning computations are performed. This approach promises faster computation speeds and significant reductions in power consumption. Such advancements are crucial today, with the ever-increasing demand for efficient and powerful computing solutions.
MemComputing
This company offers a unique computing architecture inspired by the human brain's neurons and synapses. Their technology is tailored to solve complex optimization problems using memory elements for both computation and storage.
Applied Brain Research (ABR)
Specializing in neuromorphic engineering and software, ABR has developed tools like Nengo, a neural simulator for designing and testing large-scale brain models suitable for applications ranging from robotics to AI. Their flagship software, Nengo, stands out as a neural simulator designed for crafting and testing expansive brain models. This software has found applications in diverse fields, from robotics to artificial intelligence, underscoring the versatility and potential of neuromorphic systems.
Knowm
With a focus on memristor-based solutions for neuromorphic computing, Knowm provides a platform for building adaptive learning systems that can evolve and learn over time.
CogniMem
This company, specializing in pattern recognition, has developed technologies based on neuromorphic computing principles. Their products are designed to mimic the human brain's ability to recognize patterns and learn from experience.
Neurala
Developing deep-learning neural network software for devices like drones and cameras, Neurala’s technology aims to make these devices more autonomous and capable of real-time learning and adaptation.
Neurala is a pioneering company in deep-learning neural network software specifically tailored to devices such as drones, cameras, and similar technologies. Their flagship technology, the “Neurala Brain,” is engineered to endow devices like drones with enhanced autonomy, empowering them to learn and adapt in real-time. This approach to neuromorphic computing aims to integrate software into various devices, rendering them more intelligent and reactive to their surroundings. Analog computing, on the other hand, operates based on continuous variables and physical phenomena, representing data as varying physical quantities. In the context of modern analog computers, they are often designed to emulate the brain's neural structures, providing efficient solutions for tasks like pattern recognition and sensory processing.
Neurala’s approach to neuromorphic computing is unique in that it focuses on software that can be seamlessly integrated into various devices. This makes these devices more intelligent and enhances their responsiveness to environmental stimuli.
Summary of Analog Computing Companies
In conclusion, while analog computing might seem like a relic of the past, its principles are finding new life in modern applications, especially in neuromorphic computing. The companies listed above are just a few pioneers leading the charge in this exciting field. Read another Article about Analog Computing, what it is, and how it relates to Quantum Computing.”
No, it’s hot stuff.
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