#mooreslaw — Public Fediverse posts

https://www.technologyreview.com/2026/06/25/1139696/ibm-unveils-sub1nm-chip/

#computer #quantum #ibm #chip #architecture #nanostack

Research Network Digi-Oek.ch @[email protected] · 2026-06-30 · 17:54 UTC

[en] Moore’s Law? When #computer chips can't get smaller (can they?)

"... in the last 15 years, transistors have gotten close to the point where #quantum mechanics starts to interfere with their function: just a few dozen nanometers in size. They can’t get smaller."

"... approach familiar to urban planners: build up. On Thursday, #IBM announced it has created a #chip that uses this strategy. The new #architecture, known as a #nanostack, vertically stacks transistors in two layers on a #silicon chip."

https://www.technologyreview.com/2026/06/25/1139696/ibm-unveils-sub1nm-chip/

#computer #quantum #ibm #chip #architecture #nanostack

Research Network Digi-Oek.ch @[email protected] · 2026-06-30 · 17:54 UTC

[en] Moore’s Law? When #computer chips can't get smaller (can they?)

"... in the last 15 years, transistors have gotten close to the point where #quantum mechanics starts to interfere with their function: just a few dozen nanometers in size. They can’t get smaller."

"... approach familiar to urban planners: build up. On Thursday, #IBM announced it has created a #chip that uses this strategy. The new #architecture, known as a #nanostack, vertically stacks transistors in two layers on a #silicon chip."

https://www.technologyreview.com/2026/06/25/1139696/ibm-unveils-sub1nm-chip/

#quantummechanics #mooreslaw #moore #transistor #silicon #nanostack

Research Network Digi-Oek.ch @[email protected] · 2026-06-30 · 17:54 UTC

[en] Moore’s Law? When #computer chips can't get smaller (can they?)

"... in the last 15 years, transistors have gotten close to the point where #quantum mechanics starts to interfere with their function: just a few dozen nanometers in size. They can’t get smaller."

"... approach familiar to urban planners: build up. On Thursday, #IBM announced it has created a #chip that uses this strategy. The new #architecture, known as a #nanostack, vertically stacks transistors in two layers on a #silicon chip."

https://www.science.org/content/article/engineers-cram-100-billion-transistors-microchip

#computer #quantum #ibm #chip #architecture #nanostack

Onno (VK6FLAB) @[email protected] · 2026-06-30 · 12:51 UTC

IBM: Let's stack transistors in a chip and cram 100 billion transistors in a square centimetre.

https://www.science.org/content/article/engineers-cram-100-billion-transistors-microchip

#mooreslaw #microprocessor #science

Onno (VK6FLAB) @[email protected] · 2026-06-30 · 12:51 UTC

IBM: Let's stack transistors in a chip and cram 100 billion transistors in a square centimetre.

https://www.science.org/content/article/engineers-cram-100-billion-transistors-microchip

#mooreslaw #microprocessor #science

Onno (VK6FLAB) @[email protected] · 2026-06-30 · 12:51 UTC

IBM: Let's stack transistors in a chip and cram 100 billion transistors in a square centimetre.

https://www.science.org/content/article/engineers-cram-100-billion-transistors-microchip

Onno (VK6FLAB) @[email protected] · 2026-06-30 · 12:51 UTC

IBM: Let's stack transistors in a chip and cram 100 billion transistors in a square centimetre.

https://proe.whimm.ing/data-centers-kill-dolphins/

#mooreslaw #microprocessor #science

PRoe @[email protected] · 2026-06-09 · 10:02 UTC

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 […]

#mooreslaw #o #opinion #opionion #technology #twilightzone

PRoe @[email protected] · 2026-06-09 · 10:02 UTC

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 […]

https://proe.whimm.ing/data-centers-kill-dolphins/

#mooreslaw #o #opinion #opionion #technology #twilightzone

PRoe @[email protected] · 2026-06-09 · 10:02 UTC

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 […]

https://proe.whimm.ing/data-centers-kill-dolphins/

#usa #twilightzone #technology #opionion #opinion #o

Miguel Afonso Caetano 🦾 🌹 @[email protected] · 2026-06-02 · 21:20 UTC

#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

#openaccess #automation #marxism #politicaleconomy #mooreslaw #siliconvalley

Miguel Afonso Caetano 🦾 🌹 @[email protected] · 2026-06-02 · 21:20 UTC

#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

#openaccess #automation #marxism #politicaleconomy #mooreslaw #siliconvalley

Miguel Afonso Caetano 🦾 🌹 @[email protected] · 2026-06-02 · 21:20 UTC

#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

#employment #siliconvalley #mooreslaw #politicaleconomy #marxism #automation

Miguel Afonso Caetano 🦾 🌹 @[email protected] · 2026-06-02 · 21:20 UTC

#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

#openaccess #automation #marxism #politicaleconomy #mooreslaw #siliconvalley

NewsNews AI @[email protected] · 2026-05-30 · 11:01 UTC

Researchers develop a 3D silicon stacking process, allowing circuits to be layered to increase computing power and extend Moore's Law

A new manufacturing process using ultra-thin silicon membranes and low-temperature techniques allows for the stacking of circuits in multiple layers.

https://newsnews.ai/article/3d-silicon-chip-breakthrough-moores-law

https://newsnews.ai/article/3d-silicon-chip-breakthrough-moores-law

#semiconductors #mooreslaw #technology

NewsNews AI @[email protected] · 2026-05-30 · 11:01 UTC

Researchers develop a 3D silicon stacking process, allowing circuits to be layered to increase computing power and extend Moore's Law

A new manufacturing process using ultra-thin silicon membranes and low-temperature techniques allows for the stacking of circuits in multiple layers.

https://newsnews.ai/article/3d-silicon-chip-breakthrough-moores-law

#semiconductors #mooreslaw #technology

NewsNews AI @[email protected] · 2026-05-30 · 11:01 UTC

Researchers develop a 3D silicon stacking process, allowing circuits to be layered to increase computing power and extend Moore's Law

A new manufacturing process using ultra-thin silicon membranes and low-temperature techniques allows for the stacking of circuits in multiple layers.

https://newsnews.ai/article/3d-silicon-chip-breakthrough-moores-law

#technology #mooreslaw #semiconductors

NewsNews AI @[email protected] · 2026-05-30 · 11:01 UTC

Researchers develop a 3D silicon stacking process, allowing circuits to be layered to increase computing power and extend Moore's Law

A new manufacturing process using ultra-thin silicon membranes and low-temperature techniques allows for the stacking of circuits in multiple layers.

https://soyacincau.com/2026/05/25/huawei-tau-scaling-law-logicfolding-chip-architecture/

#semiconductors #mooreslaw #technology

SoyaCincau @[email protected] · 2026-05-25 · 16:00 UTC

Tau Scaling: Huawei’s radical architectural approach to overcome US sanctions #computers #digitallife #featured #hetingbo #huawei #kirin #laptops #logicfolding #mooreslaw #news #smartphones #tauscalinglaw #tech

#computers #hetingbo #huawei #kirin #laptops #logicfolding

SoyaCincau @[email protected] · 2026-05-25 · 16:00 UTC

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/

#featured #digitallife #tech #tauscalinglaw #smartphones #news

SoyaCincau @[email protected] · 2026-05-25 · 16:00 UTC

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/

#computers #hetingbo #huawei #kirin #laptops #logicfolding

Open Risk @[email protected] · 2026-05-12 · 08:35 UTC

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

https://openai.com/index/mrc-supercomputer-networking/

#datacenters #llm #mooreslaw #supercomputing

Open Risk @[email protected] · 2026-05-12 · 08:35 UTC

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

https://openai.com/index/mrc-supercomputer-networking/

#datacenters #llm #mooreslaw #supercomputing

Open Risk @[email protected] · 2026-05-12 · 08:35 UTC

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

https://openai.com/index/mrc-supercomputer-networking/

#supercomputing #mooreslaw #llm #datacenters

Open Risk @[email protected] · 2026-05-12 · 08:35 UTC

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

https://openai.com/index/mrc-supercomputer-networking/

#datacenters #llm #mooreslaw #supercomputing

NieuweMobiel.NL @[email protected] · 2026-04-19 · 06:41 UTC

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?

#MooresLaw #Techweetje

#mooreslaw #techweetje

muddle 🥣 @[email protected] · 2026-04-10 · 23:45 UTC

@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...

#mooreslaw #machinelearning #classifiers

muddle 🥣 @[email protected] · 2026-04-10 · 23:45 UTC

@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...

#mooreslaw #machinelearning #classifiers

muddle 🥣 @[email protected] · 2026-04-10 · 23:45 UTC

@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...

#classifiers #machinelearning #mooreslaw

muddle 🥣 @[email protected] · 2026-04-10 · 23:45 UTC

@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...