Interactive animations comparing GPU-style stored program architectures with Domain Flow dataflow architectures for AI inference
Modern GPUs are powerful, but they pay a heavy tax for their flexibility. To handle memory latency, GPUs maintain thousands of threads, each with dedicated registers, managed by complex schedulers. Only ~12% of a GPU's energy goes to actual computation - the rest is instruction overhead.
Domain Flow architectures take a different approach. Instead of hiding latency with thread-level parallelism, they eliminate it through system-level scheduling. Data flows through compute in predictable patterns, controlled by credits rather than instructions. The result: 6-7× better energy efficiency for AI inference workloads.
See how GPUs juggle thousands of threads to hide memory latency, while Domain Flow eliminates latency at the system level.
Compare chip floorplans to see where transistors go. GPUs need massive infrastructure; Domain Flow puts silicon where it matters.
Follow the energy from input to output. See how GPU instruction overhead burns power, while Domain Flow maximizes useful work.
AI inference is dominated by matrix operations: matrix multiply, convolutions, attention. These are domain-specific workloads with predictable data access patterns. GPUs treat them as general-purpose programs, paying the full instruction tax on every operation.
Domain Flow recognizes that AI inference is a data transformation problem, not a control flow problem. By scheduling data movement at the system level and flowing data through compute in predictable patterns, we eliminate the need for instruction infrastructure entirely.
The result: Same compute, fraction of the power. This enables AI at the edge, in mobile devices, and in data centers where power is the limiting factor.