Eight engines turn a fixed pipeline into one that observes the workload and reshapes itself — deterministically, and with evidence.
Adaptive routing. Chooses execution paths at runtime from measured congestion and traffic prediction instead of a fixed schedule — balancing work across active memory banks.
Learns stride and reuse patterns to prefetch the data a workload is about to need, cutting redundant fetches on bandwidth-bound work — and reports its prediction confidence.
Detects zero lanes and skips the work entirely, turning sparsity directly into saved cycles and power rather than wasted multiply-accumulates.
Selects numeric precision per operation across 8/16/32-bit, so compute-bound work runs lean without sacrificing the accuracy a workload actually needs.
A closed loop that turns observation into action — adjusting routing, precision, cache and sparse thresholds while the workload runs, then scoring the result.
Every decision is explained: a timeline of input, decision, reason and confidence per stage, with a confidence heatmap and root-cause view.
Project XMCP's behavior onto your CPU/GPU/FPGA, memory and power envelope using a run's measured adaptivity — clearly labeled as a projection for planning.
Fast Simulation for interactive iteration; RTL Validation runs real SystemVerilog through iverilog for functional sign-off — both in one workflow.