Open weights-to-masks compiler: neural network checkpoints into mask-programmed compute-in-ROM silicon. Kernel release; the full compiler ships with the paper.
Project description
Ankhdjet
Ankhdjet is an open compiler that turns neural network checkpoints into mask-programmed compute-in-ROM silicon: a model becomes a mask set, and a model update becomes a mask respin rather than a re-implementation.
This 0.0.x release is a name-reserving kernel: it ships the package skeleton, a command-line banner, and a reference ternary quantizer (BitNet-style absmean). The full compiler (checkpoint to ternary IR to SystemVerilog to signed-off GDS on open PDKs, with its bit-exact verification chain) is released alongside the accompanying paper.
The first fabrication vehicle (a 64x32 ternary compute-in-ROM read demonstrator) is submitted on the TinyTapeout ttsky26c shuttle, with silicon expected in March 2027.
Quantize a matrix to ternary weights with per-tensor scale:
from ankhdjet import ternary_quantize
w, scale = ternary_quantize([[0.4, -1.2, 0.05], [0.9, -0.3, 0.0]])
License: Apache-2.0.
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