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cpldcpu

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cpldcpu.github.io 4mo ago

Towards Self-Replication: Claude Opus Designs Hardware to Run Itself

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cpldcpu.github.io 5mo ago

Glowing Polyhedrons – LED filament 3D objects using graph theory

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github.com 8mo ago

MODPlayRISCV – Playing tracker Music on ultra-low-end RISC-V MCUs

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cpldcpu.com 11mo ago

Candle Flame Oscillations as a Clock

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339pts77
huggingface.co 1y ago

MiniCPM4 – a series of open multimodal models for edge inference

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machinelearning.apple.com 1y ago

Updates to Apple's On-Device and Server Foundation Language Models

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www.reuters.com 1y ago

Meta delays release of its 'Behemoth' AI model

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www.wsj.com 1y ago

Meta Is Delaying the Rollout of Its Flagship AI Model

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arxiv.org 1y ago

Matrix-vector multiplication implemented in off-the-shelf DRAM for Low-Bit LLMs

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230pts53
www.wsj.com 1y ago

Anthropic CEO Says AI Could Surpass Human Intelligence by 2027

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github.com 1y ago

Misguided Attention – Prompts to challenge the reasoning ability of LLMs

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cpldcpu.github.io 1y ago

Multi-Layer Perceptron Visualization

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github.com 1y ago

Claude-Artifacts-Starter: Deploy Claude Artifacts to GitHub Pages

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cpldcpu.wordpress.com 1y ago

Implementing neural networks on the "3 cent" 8-bit microcontroller

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159pts20
www.nrel.gov 2y ago

Interactive Best Research Solar-Cell Efficiency Chart

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www.pv-magazine.com 2y ago

34.6% efficient perovskite-silicon tandem solar cell demonstrated

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cpldcpu.com 2y ago

Neural Network inference on a "3-cent" 8 bit Microcontroller

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cpldcpu.wordpress.com 4y ago

Controlling RGB LEDs with only powerlines: Anatomy of a Christmas light string

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152pts51
cpldcpu.wordpress.com 4y ago

Controlling RGB LEDs with only powerlines: Anatomy of a Christmas light string

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1pts0

When Intel finally shipped it at the 45-nanometer node in 2007, Gordon Moore called it the biggest change in transistor technology since the late 1960s. The breakthrough was not the material. It was learning how to process the material at scale.

Its curious that they picked this example. The challenge with HKMG was not the material itself, but how to integrate into into the transistor stack.

There were two completely different approaches: Gate first and replacement gate. Gate first is what the industry was already using for silicon oxide so everybody tried to go with as little change as possible. Only intel decided for replacement gate, which worked much better and reaped some other benefits on the way.

This was a watershed moment in the industry and ultimately led to some of the players dropping out of the cmos race.

But is this really a "scale-up" problem? It required development of novel manufacturing processes (atomic layer deposition), but was still mainly a process integration and device engineering topic.

The part of the thesis I have to agree with is that there is a data problem. The development above relies on executing lots of time consuming and tedious split experiments that often cannot be parallelized. The outcome of this relies heavily on the experience and diligence of the experimenters.

It's probably well suited for an "autoresearch" approach, bridging to the phyiscal world and dealing with the timescale is the challenge.

Great article!

Yeah, that pattern can be seen everywhere in semiconductors. E.g. the transistor invention vs. Lilienfeld, Heil, Matare etc. So the scope is more narrow than "Inventend Semiconductors".

Generally, there seems to be a tendency to disregard discoveries from outside the US. I think this pattern can still be observed today...

Other examples: Invention of light bulb, telephone.

Wow! And it also implements a very interesting variant of SUBLEQ that is turing complete.

This VM implements an OISC - a One Instruction Set Computer. That instruction takes three signed 32-bit operands, a, b and c, and runs a program from memory m[] as follows:

1 PC (program counter) starts at 0

2 Fetch the next instruction (32-bit signed operands a, b and c)

3 If the low bit on any operand is set, remove it, and replace that operand with m[operand] i.e., a dereference of that address

4 Set m[b] = m[b] - m[a]

5 If m[b] is 0 or negative, set the PC to c, otherwise increment PC by 3 words

6 Go to step 2

What do you mean with "open-source"? Of course, the inference code for all the open weight models is publically available - see llama.cpp or hf transformers.

There are, however, very few models where also the full training pipeline is available. Olmo by AI2 comes to mind.

I had to give it a try.

Claude, the ole cheater, recognized what the file was, downloaded the psid from the web, found a wasm sid player and built a website around it:

https://claude.ai/public/artifacts/df6cdcae-08dc-452b-ba19-f...

https://claude.ai/share/4dd36c16-bc62-445a-b423-ad4637f06432

GPT-5.5 built a lot of python scripts to extract the music data. Strudel implementation failed, but I then asked it to build a website:

https://ubiquitous-vacherin-8e7993.netlify.app/

This is a translation of the music into javascript based on the assembler source.

Really impressive on both accounts. Some iterations were requied for both.

I wonder how well this works with MoE architectures?

For dense LLMs, like llama-3.1-8B, you profit a lot from having all the weights available close to the actual multiply-accumulate hardware.

With MoE, it is rather like a memory lookup. Instead of a 1:1 pairing of MACs to stored weights, you suddenly are forced to have a large memory block next to a small MAC block. And once this mismatch becomes large enough, there is a huge gain by using a highly optimized memory process for the memory instead of mask ROM.

At that point we are back to a chiplet approach...

May be worth pointing out, that this is not the first residual connection innovation to be in production.

Gemma 3n is also using a low-rank projection of the residual stream called LAuReL. Google did not publicize this too much, I noted it when poking around in the model file.

https://arxiv.org/pdf/2411.07501v3

https://old.reddit.com/r/LocalLLaMA/comments/1kuy45r/gemma_3...

Seems to be what they call LAuReL-LR in the paper, with D=2048 and R=64

This lists many transistor patents from oldest to newest.

https://patents.google.com/?q=(H03F3%2f16)&sort=old

The Matare/Welker Patent is missing though

https://patents.google.com/patent/US2673948A/en.541

The entire debate is tiring. It would be better if these reviews would put the actual device physics of the different concepts into context.

Is there any report of a reproduction of the device proposed by Lilienfeld in his patents? If he managed to make functional devices back then, it should be possible today? (Note: Cu2S is not a very well controllable semiconductor...)

Edit:

Gemini Deep Research summary here, its quite informative: https://docs.google.com/document/d/1jE0wQVeWP9Eiybh_C6zMKeZ5...

Also specifically on Cu based TFT: https://docs.google.com/document/d/1_B2x2gBPKgGFVgJyQ0qzPdI4...

From the second document: "The primary obstacle for $Cu_2S$ TFTs is degeneracy. Spontaneous copper vacancies form with negligible energy cost in the sulfur lattice. As a result, stoichiometric $Cu_2S$ is thermodynamically unstable in air, rapidly oxidizing or losing copper to form substoichiometric phases ($Cu_{2-x}S$) with hole concentrations exceeding $10^{20}-10^{21} \text{ cm}^{-3}$."

This explains why there are zero reproductions of Lilienfelds devices. It should be noted that Lilienfeld is one of the inventors of electrolytic capacitors and did therefore know where well how to create extremely thin insulating layers as needed for TFTs. It is not impossible to assume that he could have used other semiconductors (e.g. CdS) with his concept. However, the patents seems to specifically mention Cu2S, which does not yield functional TFTs.

What also cannot be ignored, is that transformer models are a great unifying force. It's basically one architecture that can be used for many purposes.

This eliminates the need for more specialized models and the associated engineering and optimizations for their infrastructure needs.

I am not a professional software developer but instead more of multi-domain system architect and I have to say it is absolutely magical!

The public discourse about LLM assisted coding is often driven by front end developers or rather non-professionals trying to build web apps, but the value it brings to prototyping system concepts across hardware/software domains can hardly be understated.

Instead of trying to find suitable simulation environments and trying to couple them, I can simply whip up a gui based tool to play around with whatever signal chain/optimization problem/control I want to investigate. Usually I would have to find/hire people to do this, but using LLMs I can iterate ideas at a crazy cadence.

Later, implementation does of course require proper engineering.

That said, it is often confusing how different models are hyped. As mentioned, there is an overt focus on front end design etc. For the work I am doing, I found Claude 4.5 (both models) to be absolutely unchallenged. Gemini 3 Pro is also getting there, but long term agentic capability still needs to catch up. GPT 5.1/codex is excellent for brainstorming in the UX, but I found it too unresponsive and intransparent as a code assistant. It does not even matter if it can solve bugs other llms cannot find, because you should not put yourself into a situation where you don't understand the system you are building.