Neuromorphic AI hardware encompasses brain-inspired computing systems that implement neural network primitives directly in physical substrates to achieve extreme energy efficiency and low latency. Architectures use devices such as memristors, magnetic tunnel junctions, electrochemical memories, photonic and microwave components, and organic or superconducting neurons to realize synapses, neurons, and compute-in-memory operations. These platforms support spiking and analog neural computation, on-chip learning, and specialized sensory and cognitive functions, targeting applications from edge intelligence and autonomous systems to large-scale AI acceleration and brain–computer interfaces.
Robotics
In the midst of the co-development of artificial intelligence and robotic advancements, developing technologies that enable robots to efficiently perceive and respond to their surroundings like humans has become a crucial ...
Jul 16, 2025
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Energy & Green Tech
The artificial intelligence industry is scrambling to reduce its massive energy consumption through better cooling systems, more efficient computer chips, and smarter programming—all while AI usage explodes worldwide.
Jul 15, 2025
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Energy & Green Tech
Artificial intelligence (AI) can be made more sustainable by making practical changes, such as reducing the number of decimal places used in AI models, shortening responses, and using smaller AI models, according to research ...
Jul 9, 2025
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Hardware
The latest generative AI models such as OpenAI's ChatGPT-4 and Google's Gemini 2.5 require not only high memory bandwidth but also large memory capacity. This is why generative AI cloud operating companies like Microsoft ...
Jul 7, 2025
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Energy & Green Tech
It's estimated it can take an AI model over 6,000 joules of energy to generate a single text response. By comparison, your brain needs just 20 joules every second to keep you alive and cognitive.
Jul 1, 2025
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Hardware
Artificial intelligence is considered to be computationally and energy-intensive—a challenge for the Internet of Things (IoT), where small, embedded sensors have to make do with limited computing power, little memory and ...
Jun 26, 2025
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Electronics & Semiconductors
In recent decades, computer scientists have been developing increasingly advanced machine learning techniques that can learn to predict specific patterns or effectively complete tasks by analyzing large amounts of data. Yet ...
Hardware
BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the Institute of Science Tokyo, ...
Jun 23, 2025
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Hardware
A novel power supply technology for 3D-integrated chips has been developed by employing a three-dimensionally stacked computing architecture consisting of processing units placed directly above stacks of dynamic random access ...
Jun 20, 2025
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Robotics
Robots are increasingly becoming a part of our lives—from warehouse automation to robotic vacuum cleaners. And just like humans, robots need to know where they are to reliably navigate from A to B.
Jun 19, 2025
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