Logic-based neural inference is often assumed to be hardware-efficient, yet its gate-level cost has rarely been validated beyond FPGA-resource abstractions. We present a cross-layer framework connecting quantized neural network training to logic- and circuit-level hardware evaluation. Low-bit sparse MLPs are trained under signed and unsigned quantization; gate-level Boolean synthesis of extracted truth tables yields hundreds of gates per first-layer neuron in the evaluated configurations, indicating limited gate-level scalability for direct truth-table realization. More importantly, low-bit signed quantization transforms each weight-activation product from a general multiplication into a conditional add/subtract/shift primitive, a structural regularization that induces arithmetic regularity across the network. This regularity is precisely what Cell Design Methodology (CDM) supergates are designed to exploit: by merging transistor stacks of repeated arithmetic patterns, CDM achieves up to 47% lower transistor count and 77% lower power at the representative arithmetic-block level in GF22nm; when projected to the network level using those measured primitives, FOM gains of up to 26 × are observed across four benchmarks. Signed quantization consistently outperforms unsigned in accuracy. These results establish a coherent cross-layer principle: quantization induces structure, structured arithmetic enables compact MAC realization, and CDM provides an efficient transistor-level implementation of that structure.

We present an end-to-end hardware agent that translates multimodal specifications including free-form text, PDF, DOCX, audio, and video into verified hardware across four levels of abstraction: behavioral RTL, gate-level netlist with GLS, transistor-level CMOS SPICE verified by ngspice, and layout-preview SVG with ITRS-based physical estimates (area, power, latency, energy) across configurable technology nodes (130 nm–5 nm). A novel Cell Design Methodology (CDM) supergate knowledge-injection mechanism embeds a structured manifest of multi-output, multi-functional CDM cell patterns into the generation and repair stages, grounding transistor-level synthesis in provably correct pmos/nmos topologies rather than unconstrained behavioral inference. When processing multi-source inputs. A structured intermediate representation canonicalizes all inputs, and Gemini Embeddings rank evidence chunks for retrieval-augmented specification extraction. A two-oracle strategy uses a deterministic spec-derived testbench as the primary oracle and a smoke testbench as fallback. Verification is performed entirely by external tools, including Verilator, Icarus Verilog, Yosys, SymbiYosys, OpenSTA, and ngspice. The agent reports RTL complexity metrics, transistor counts, layout dimensions, physical estimates, formal assertions, and per-iteration audit data for reproducibility.