Tensor networks map interactions among design variables, constraints, and objectives to guide faster, better-informed design synthesis.
Auxiliary parallel solves and stored warm starts shorten hierarchical supply chain LPP re-solving without reducing plan quality.
By splitting DFT work between MMA circuitry and GPU circuits, arbitrary transform sizes run faster with lower time and resource use.
Extracted dimension and operation features are scored to recommend suitable dataset analysis patterns, cutting manual setup and expertise needs.
In-memory NXOR and majority sensing enable single-cycle binary matrix multiplication, cutting data movement, latency, and energy in neural networks.
Small-granularity submatrix LU factorization preserves large matrix-multiply dimensions to balance process load and improve dense matrix efficiency.
Non-differentiable DVH criteria make radiotherapy planning slow; quantile regression embeds them in nested optimization for faster, precise plans.
Separate FFTs can lose coherence across chirp sequences; joint Hankel processing improves radar velocity estimation.