hm.. has been quite a while for me. The good thing about the Tang Nano is that it is supported by the Yosys open source toolchain. There are quite a few resources on the web when you search for the combination.
I had Opus 4.5 design an LLM inference engine in verilog, including firmware and automated verification a while ago: https://github.com/cpldcpu/smollm.c
It's of course far from optical. But lowering the implementation through the abstraction levels turned out to be extremely powerful.
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
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.
They mentioned that they using strong quantization (iirc 3bit) and that the model was degradeted from that. Also, they don't have to use transistors to store the bits.