No tricks or substitutions; every benchmark runs the complete network graph with validated accuracy. See the source code, intermediate representations, and full results.
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CNNs, ViTs, LLMs, Detectors, Segmenters & more
Full end-to-end graphs, no substitutions
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Most NPU accelerators were optimized for ResNet-style networks with simple, repetitive convolutions stacked in sequence. Modern AI is much more complex.
Simple, repetitive 3×3 convolutions with fixed dataflow patterns.
Multi-head attention, LayerNorm, MatMul, and complex branching graphs.
ResNets to Transformers to what's next. AI is always evolving.
Fixed architectures optimized for yesterday's models that break with every major AI shift.
Fully programmable, able to run any operator or graph today and tomorrow.
Our benchmark library grows every quarter with new models across all categories, from classic CNNs to cutting‑edge vision transformers and language models.
Every model tested against reference implementations
Access intermediate code and complete network graphs
New models added as the AI landscape evolves
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