"The next 15 years of Moore's Law, according to Imec."
"The next evolution in CMOS transistors, the kind in almost all chips on the planet, will be the complementary field-effect transistor (CFET), and Imec predicts its commercial introduction will begin around 2033."
Data centers kill Dolphins!
Ok, now that the Algos like me, No they don't, I want to say some words that for some reason are not being said? Words that I learned from a text book, but the words "All People Seem To Need Data Processing" Those were not taught as a sentence but a numeric. The point in the sentence was assumed by everyone, up until 2026 when Americans decided they HATE data processing. The OSI Model: Application Presentation Session Transport Network Data Link Physical It is the foundation to […]
#OpenAccess book ->
100% Utilization: Computation and Labor After Moore's Law
Andrew Lison
(MIT Press, 2026)
https://mitpress.mit.edu/9780262051361/100-utilization/
"A wide-ranging analysis of how the material limits to discrete, silicon-based computing power impact employment and automation
Since the end of the Second World War, we have come to expect continual growth in computing power and the rapid development of digital technology. This dynamic has enabled informational procedures to supplant an ever-increasing range of human and mechanical activity. However, indications that the semiconductor industry is approaching the physical limits of integrated circuitry pose an existential challenge to Intel Corporation cofounder Gordon Moore’s “law,” which prescribes an exponential increase in microchip density—and, by extension, processing performance—every two years.
Placing theories of employment in dialectical conjunction with the concrete operations of computing, 100% Utilization explores the consequences of pushing processing power to its limits for a culture seemingly reliant on automation as much as human labor. In accounting for this contradiction, Andrew Lison offers a corrective to theories of digital mediation, emphasizing its symbolic and representational capabilities. He connects the looming end of Moore’s law to trends in semiconductor manufacturing, custom hardware, and parallelized software techniques, including AI. Ultimately, he traces this historical technological boom and impending bust through the racialized history of Silicon Valley to longer-term conceptions of the relationship between machinery and labor."
#Automation #Marxism #PoliticalEconomy #MooresLaw #SiliconValley #Employment
Tau Scaling: Huawei’s radical architectural approach to overcome US sanctions #computers #digitallife #featured #hetingbo #huawei #kirin #laptops #logicfolding #mooreslaw #news #smartphones #tauscalinglaw #tech
https://soyacincau.com/2026/05/25/huawei-tau-scaling-law-logicfolding-chip-architecture/
Intel and AMD: How the Chip That Runs Everything Came to Be
The "AI" phenomenon and craze has been discussed endlessly, including the vast computational and energy requirements of dedicated #datacenters
There is an important split betwen model building and inference of statistical models (and hence also #llm ) which means there is not one but two problems to solve.
The first one is *highly* non-trivial. It bumps against #mooreslaw and requires #supercomputing networking tricks. The datacenter must operate as one giant machine
Voelt jouw telefoon na een jaar alweer oud? 🤔
Op deze dag in 1965 werd Moore's Law gepubliceerd! De 'wet' die voorspelt dat de kracht van chips elke twee jaar verdubbelt. De échte reden waarom we maar blijven upgraden. 🚀
Welk 'prehistorisch' toestel mis jij stiekem nog?
@icing There's a thing called "the curse of dimensionality" and it applies to neural networks. I guess you could say that it's like a reverse Moore's Law but for neural nets. Basically, (and this is just my mostly-non technical explanation), neural nets are basically huge multi-dimensional classifiers and when you need to do backpropagation to train the net, it involves making small adjustments to localised areas of the classifier space. The problem (or curse) of having more dimensions is that it becomes harder and harder to localise the changes because every local space becomes closer to all the other points in every other subspace. This means exponentially higher training costs as these models scale.
At least that's as I understand it. I'm not a mathematician, but I have read plenty of stuff relating to machine learning over the years (since the 90s) and I think I've got the above right...
MARIA, the graphics chip in the Atari 7800, had 24k transistors. Atari sold about 3.8M units of that console, which adds up to 91.2B transistors.
Coincidentally, GB202-300, the graphics chip in the NVIDIA 5090, has 92.2B transistors.
Just sayin'
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