Two decades on from the first reported covalent organic frameworks, Nina Notman investigates what their future holds.
3D integrated circuits promise smaller, faster devices with lower power consumption. Vertically stacked 3D integrated circuits also enable novel in-memory and in-sensor computing paradigms and incorporate functionally diverse materials, which can benefit many edge applications. There are several complementary approaches to 3D integration. For example, 3D heterogeneous integration involves stacking and interconnecting multiple chips, each potentially made from different materials or optimized for different functions, within a single package. On the other hand, 3D monolithic integration refers to fabricating layers of transistors sequentially on a single wafer, creating a more seamless and compact structure. This approach offers even greater density and performance benefits by reducing interlayer distances and improving signal integrity. Both techniques are crucial for advancing the next generation of high-performance, energy-efficient electronic devices and require interdisciplinary collaborations across materials science, electrical engineering, and semiconductor manufacturing.
In this Communications Engineering collection, we aim to drive research in the engineering side of 3D integration by bringing together the following topics of interest:
Well this is like the wizard of odd. 🙄 An expedition to a deep-sea ridge, just north of the Hawaiian Islands, revealed a surprise discovery back in 2022: an ancient dried-out lake bed paved with what looks like a yellow brick road.
The eerie scene was chanced upon by the exploration vessel Nautilus, while surveying the Liliʻuokalani ridge within Papahānaumokuākea Marine National Monument (PMNM).
Ve only explored about 3 percent of its seafloor…
This Deep Dive AI podcast discusses The Origins of Us: Evolutionary Emergence and The Omega Point Cosmology by Alex M. Vikoulov, Book I of The Science and Philosophy of Information eBook/audiobook series. This book serves as both an accessible introduction and a standalone work, exploring some of the most profound questions in science and philosophy.
In this epic work, Vikoulov delves into the origins of life, consciousness, and intelligence, examining topics such as abiogenesis, noogenesis, and the rise of Homo sapiens. The book also presents The Omega Point Cosmology, which envisions a teleological progression of intelligence toward a cosmic destiny. It blends scientific exploration with digital physics, complexity theory, and transcendental metaphysics, offering a novel perspective on the interconnectedness of information, mind, and reality.
*The Origins of Us: Evolutionary Emergence and the Omega Point Cosmology by Alex M. Vikoulov is available as a Kindle eBook and Audible audiobook:
#OriginsOfUs #EvolutionaryEmergence #OmegaPointCosmology #SyntellectHypothesis #DigitalPhysics #HomoSapiens #ScienceOfInformation #PhilosophyOfInformation #AlphaPoint #OmegaPoint #abiogenesis #noogenesis #evolution #consciousness
Astronomers have identified a remarkable water reservoir hidden in a corner of the cosmos, circling a quasar more than 12 billion light-years away.
At that distance, the light we see today began its journey not too long after the universe itself formed.
The water supply in this distant place is huge, containing the equivalent of about 140 trillion times all the water in Earth’s oceans combined.
For decades, scientists have focused on amyloid plaques—abnormal clumps of misfolded proteins that accumulate between neurons—as a therapeutic target for Alzheimer’s disease. But anti-amyloid therapies haven’t made strong headway in treating the devastating condition.
Now, researchers at Yale School of Medicine (YSM) are zeroing in on a byproduct of these plaques, called axonal spheroids, and exploring how to reverse their growth. They published their findings March 10 in Nature Aging.
Axonal spheroids are bubble-like structures on axons—the part of the neuron that sends messages through electrical impulses—that form due to swelling induced by amyloid plaques. Previous research at YSM has shown that as these spheroids grow, they block electricity conduction in the axons, which can hinder the ability to communicate with other neurons.
This work presents a formal theory of consciousness, showing how quantum mechanics emerges from singularity, multiplicity, and trinity.
Former Google CEO Eric Schmidt is taking over as the CEO of Relativity Space, a 9-year-old rocket startup, a company spokesperson confirmed in a statement to TechCrunch. This is Schmidt’s first CEO job since he left Google nearly 15 years ago.
On Monday, Schmidt told employees of Relativity Space that he made a significant investment and had taken a controlling stake in the company, The New York Times first reported.
Schmidt is succeeding the startup’s co-founder, Tim Ellis, as chief executive. In a post on X, Ellis noted he will continue to support Relativity Space as a director on the company’s board.
An artificial nerve that is based on a vertical n-type organic electrochemical transistor with a gradient-intermixed bicontinuous structure can operate at high frequencies and mimic basic conditioned reflex behaviour in animals.
Billions of people may be continuously running AI inference for their waking hours in the near future. Satisfying this demand requires relentless focus on efficiency to reduce the required quantities of two key inputs: energy and capital. The constraints on these inputs in conjunction with the slowing and/or stagnation of both Moore’s Law and Dennard Scaling has left hardware architects no choice but to pursue Domain Specific Architectures (DSAs) — architectures tailored to the task at hand.
The current dominance of GPUs in modern deep learning is largely accidental — it was pure serendipity that the computational workload of graphics and deep learning were similar. Remnants of their graphical heritage still persist in GPU architectures today. What would AI inference hardware look like if it was redesigned carte blanche? By working backwards from the AI inference workload, we can determine some optimal properties these DSAs should have. Furthermore, we will attempt to predict the direction the inference paradigm will shift over time — a crucial exercise for hardware architects and engineers alike to ensure return on investment.