TensorEDA brings tensor-network methods, uncertainty quantification, AI-guided exploration, and exact formal techniques into one coherent EDA platform—so engineering teams can explore complex design spaces with more clarity and rigor.
A connected set of engineering tools that applies a consistent research foundation across co-design, uncertainty, timing, simulation, extraction, and verification.
Import, compress, search, and sign off through a unified six-stage flow—with a reproducible decision trail.
Explore process and circuit choices together instead of sending them through disconnected loops.
Connect ngspice-driven uncertainty analysis with held-out validation and Monte Carlo comparison.
Model process, voltage, temperature, and activity as a correlated tensor—not disconnected corners.
Use exact full-chip equivalence proofs, dynamically handing difficult regions to specialized engines.
Compile circuit regions to packed BitSlice, LUT, GF(2), or gate-fallback execution based on structure and workload.
Route layout regions to analytic models or boundary-element solves based on their structure.
Combine coupling-aware graph cuts, Schur-complement macromodels, static IR drop, vectorless analysis, and electromigration checks.
Performance figures are specific to stated workflows or included benchmarks and should be evaluated against your own designs. The interactive demo uses illustrative data.
AI can help navigate a vast design space. Tensor methods and formal techniques provide the structure, validation, and confidence to make that exploration useful.
Represent high-dimensional variation and logic spaces compactly, making structure-aware analysis possible where flat representations become unwieldy.
STRUCTURE · COMPRESSIONUse polynomial chaos and adaptive sampling to explore uncertain behavior, with held-out validation and comparison against simulation baselines.
SAMPLING · VALIDATIONApply GF(2) relation tensors, treewidth-aware methods, and specialized proof engines—with exact proof paths for final equivalence checks.
PROOF · REPRODUCIBILITYUse sparse-PCE-guided co-optimization and structure-aware partitioning to prioritize promising regions of a combinatorial design space.
INFERENCE · OPTIMIZATIONSemiconductor innovation—from AI accelerators and chiplets to wide-bandgap power devices—is outpacing the corner-based and canonical-table abstractions that underpin many established workflows. TensorEDA is building a different foundation: structural tensor compression, rigorous uncertainty quantification, AI-assisted exploration, and exact algorithmic proof to help engineering teams understand correlated, high-dimensional design behavior.
Build what comes next with usPowerful methods matter only when teams can measure them, trust them, and fit them into their work.
Model variation and design space jointly wherever the mathematics calls for it—instead of breaking correlations for implementation convenience and patching them later with margin.
Put claims in context with reproducible benchmarks, real netlists, simulation runs, bug-injection tests, and transparent limitations.
Work toward familiar inputs—including Liberty, SDC, SPEF, and structural Verilog—so new analysis can complement existing engineering flows.
Explore the platform, request early access, or work with TensorEDA to evaluate a real design challenge.