Chipveda designs deterministic AI silicon for edge inference — systems that run language and vision models from fixed hardware state machines, without an operating system, a driver stack, or a scheduler standing between the model and the result.
Most AI accelerators bolt a chip onto the same runtime, driver, and scheduler stack used for training clusters — because that stack already existed. At the edge, that inheritance becomes the bottleneck: every layer between the model and the silicon is a layer that can stall, jitter, or fail open. Chipveda's architecture removes the inheritance.
The model is compiled to a hardware configuration at deploy time. From the first token onward, nothing resembling a software scheduler is in the critical path.
On-chip communication between functional blocks is encrypted and authenticated in hardware, with keys that no firmware update can ever read out.
A portion of the fabric stays reconfigurable by design — long enough to track how inference architectures evolve without a silicon respin.
Chipveda's architecture is being proven on hardware before a single mask is cut. Each stage exists to retire risk the one before it couldn't reach.
Core architecture running on commercial evaluation hardware — proving the control philosophy before committing it to silicon.
In progressMigration to a platform built for AI workloads at scale, validating the architecture against real model throughput and power targets.
NextAdvanced-node production silicon. Everything proven in stages one and two, fixed permanently into hardware.
PlannedChipveda is small on purpose. Every engineer here owns real silicon, not a slice of a slice of someone else's architecture. If the idea of inference with no scheduler in the loop sounds like the right problem to spend years on, we want to talk to you.
We share architecture detail, benchmarks, and the full development roadmap directly with qualified investors under a brief conversation first — not published here, by design. Reach out and we'll set up time.