CORTEXA
← Browse
arxivcs.ARcs.LG2026-07-04

Weave: Verified Netlist-to-Schematic Conversion via Layered Graph Layout

Senol Gulgonul

Converting a SPICE netlist into a human-readable schematic is a longstanding problem in electronic design automation: simulators and machine-learning pipelines readily produce netlists, but designers reason about circuits through diagrams. Recent learning-based approaches translate netlists into schematics probabilistically, yet they provide no guarantee that the generated drawing preserves the original connectivity, and their accuracy degrades sharply as circuits grow. We present Weave, a deterministic converter that turns a SPICE netlist into an LTspice .asc schematic using a layered (Sugiyama-style) graph layout, and that certifies every output by a round-trip connectivity check: the generated schematic is re-parsed into a netlist and compared, net for net, against the input. A result is reported as correct only when the two partitions are identical, giving a binary correctness certificate rather than a similarity score. Weave runs entirely client-side as a single dependency-free file and embeds a pin table for 5093 LTspice symbols. On the identical public Circuits-LTSpice test set used by the state-of-the-art LLM converter Schemato (117 circuits, netlisted with LTspice itself), Weave achieves 100% compilation and 100% round-trip-verified connectivity equivalence, compared with Schemato's reported 76% compilation and a graph-edit-distance similarity of 0.35; notably, 73% of that set exceeds the five-component threshold beyond which Schemato reports losing connectivity accuracy. On a larger and harder corpus, the 3460 netlistable circuits of the official Analog Devices LTspice demo collection, Weave verifies exact connectivity for 88.4% of circuits, with the remaining failures concentrated in a single, well-characterized class of dense multi-pin power modules.

View free PDFSource page

Related papers

arxivcs.ARcs.LG2026-07-07

HiFuzz: Hierarchical Reinforcement Learning for Semantic-Aware and Adaptive CPU Fuzzing

Ya Wang, Hanwei Fan, Zhenguo Liu, Xiaofeng Zhou, Yangdi Lyu, Jiang Xu, et al.

Modern processor verification struggles to reach deep architectural states due to the inefficiencies of traditional mutation-based fuzzing. We propose HiFuzz, a novel hierarchical reinforcement learning framework that replaces mutation with a structured, two-layer generation proc…

View free PDFSource page
arxivcs.LGcs.AR2026-07-22

AlphaRoute: Large Language Models as Semantic Optimizers for Multi-Objective Routing

Kabir Murjani, Mishri Bhavsar, Manish I. Patel, Jonti Talukdar

Very Large Scale Integration (VLSI) global routing is an NP-hard combinatorial optimization problem requiring signal net assignment across capacity-constrained 3D grids while minimizing congestion, wirelength, and via transitions. Because traditional heuristics rely on static pen…

View free PDFSource page
arxivcs.ARcs.AIcs.LG2026-07-17

RTL-Sequencer: Towards Scalable RTL Timing Prediction with the Sequence-based Paradigm

Ziyan Guo, Wenji Fang, Wenkai Li, Yuchao Wu, Shang Liu, Zhiyao Xie

Accurate timing prediction at the register-transfer level (RTL) is a longstanding challenge in design automation. Existing graph-based methods struggle with limited receptive fields, high complexity, and a lack of signal directionality. We present RTL-Sequencer, a novel sequence-…

View free PDFSource page
arxivcs.ARcs.LG2026-06-28

Harvesting AI Computation at the Edge via Generic Approximation

Yihan Wang, Huiru Yan, Luxin Zhang, Long Cheng, Weiwei Chen, Ying Wang, et al.

With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demand…

View free PDFSource page
arxivcs.LGcs.AR2026-07-13

HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS

Shambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous, Moritz Thoma, Judeson Anthony Fernando, Lukas Frickenstein, et al.

With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential. These methods rely on real feedback from the target hardware…

View free PDFSource page
arxivcs.AIcs.ARcs.LGcs.NE2026-07-10

A Symbolic Neural CPU for Quantization-Simulated Writeback and Interpretable Program Execution

Jose Luis Lima de Jesus Silva

Neural networks can learn algorithmic input-output mappings, but trusting a learned executor requires more than a correct final answer because the state transitions that produce it are usually hidden. To make those transitions visible, we introduce a trace-supervised symbolic neu…

View free PDFSource page