mlsanityData to 14 Oct 2026

Ankhdjet: An Open-Source Compiler for Mask-Programmed Ternary Compute-in-ROM on an Open PDK

arXiv 2608.26206 ↗First posted 26 Aug 2026cs.AR cs.ETcited by 0

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Abstract (arXiv)

Large-language-model inference is dominated by weight movement: every generated token re-reads every weight. Ternary quantization (BitNet b1.58) shrinks each weight to 1.58 bits with reported parity at the 2B-parameter scale, small enough that hardwiring the weights into a read-only mask becomes plausible, and a commercial chip (Taalas HC1) has validated hardwired weights on an advanced node behind closed tooling. This paper asks whether model-specific silicon can be made reproducible with entirely open infrastructure. We present Ankhdjet, an open-source compiler that lowers a HuggingFace ternary checkpoint (BitNet b1.58 and its kin) to a via-mask program of a fixed compute-in-ROM macro on the open SKY130 PDK, verified end to end with open tools. We defend two claims: (1) the first open-source weights-to-mask compute-in-ROM compiler on a fabricable open PDK, taken through full open-toolchain signoff (KLayout DRC zero, netgen LVS zero, clean timing) twice with two different weight matrices through an identical flow in which only the mask differs; and (2) the first compute-in-ROM macro submitted for fabrication on an open PDK (TinyTapeout/ChipFoundry SKY130 shuttle ttsky26c, silicon expected 2027). The compiler's read is fully digital (full-swing bitline sampling into standard cells, so the emission ports to any node by synthesis); an analog comparator readout of the same machine is retained as a measured variant, replaced by the digital readout at one eighth the area. We additionally report the per-level adversarial verification methodology that caught three DRC-invisible silent shorts, a negative result on storing two ternary weights per transistor at 130 nm, and which analog sense techniques survive at which nodes. Read energy is from extracted-parasitic simulation (0.98-1.73 pJ per sensed weight); no energy measurements are claimed, and the fabricated vehicles are demonstrators.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 26 Aug 2026.

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10 of 36 references.

References · 10

  1. 2024The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bitscited by 308
  2. 2025BitNet b1.58 2B4T Technical Reportcited by 38
  3. 2025Bitnet.cpp: Efficient Edge Inference for Ternary LLMscited by 22
  4. 2025TeLLMe: An Energy-Efficient Ternary LLM Accelerator for Prefilling and Decoding on Edge FPGAscited by 10
  5. 2023OpenSpike: An OpenRAM SNN Acceleratorcited by 3
  6. 2024SynDCIM: A Performance-Aware Digital Computing-in-Memory Compiler with Multi-Spec-Oriented Subcircuit Synthesiscited by 3
  7. 2025BitROM: Weight Reload-Free CiROM Architecture Towards Billion-Parameter 1.58-bit LLM Inferencecited by 3
  8. 2026OpenACM: An Open-Source SRAM-Based Approximate CiM Compilercited by 2
  9. 2026HighTide: An Agent-Curated Open-Source VLSI Benchmark Suitecited by 2
  10. 2026TOM: A Ternary Read-only Memory Accelerator for LLM-powered Edge Intelligencecited by 1

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  1. 2026Hardware Generation and Exploration of Lookup Table-Based Accelerators for 1.58-bit LLM Inference
  2. 2025AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs
  3. 2026Tied Trit-Planes: Constraining PTQTP to a Uniform Nine-Level Quantizer, with a Persistent Folded Format for Disk-Streamed Mixture-of-Experts Serving
  4. 2026FairyFuse: Multiplication-Free LLM Inference on CPUs via Fused Ternary Kernels
  5. 2026Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models
  6. 2025TerEffic: Highly Efficient Ternary LLM Inference on FPGA
  7. 2026Deterministic LLM Inference Across GPU Kernels: Power-of-Two INT8 Quantization Scales and the Limits of Tolerance-Based Conformance
  8. 2026VitaLLM: A Versatile, Ultra-Compact Ternary LLM Accelerator with Dependency-Aware Scheduling ↗