Qμβrix
quantum‑inspired neuromorphic
AI Acceleration Beyond Limits – interactive benchmark suite
inference speed 1.2× – 10×
energy efficiency 15% – 80%
hardware‑agnostic (CPU/GPU/accelerator)
Model:
Optimization depth:
100%
MLPerf v4.1 aligned
latency (ms) – lower is better
ResNet‑50 · CPU/GPU/accelerator
traditional
Qμβrix
energy efficiency (FLOPS/W) – higher is better
traditional
Qμβrix
performance radar – Qμβrix vs traditional
benchmark details – ResNet‑50
| hardware |
trad. (ms) |
Qμβrix (ms) |
speedup |
trad. F/W |
Qμβrix F/W |
gain |
MLPerf v4.1, ImageNet, LibriSpeech, SNLI
CPU: Intel Xeon 8488C, GPU: NVIDIA A100, Accelerator: Edge TPU v4
⚡ Qμβrix advantage
- 1.2× – 10× FLOPS/W on CPU/FPGA/ASIC
- ↓70% retraining (Bayesian adaptation)
- 15% – 80% less energy (edge/cloud)
- 10% – 50% lower latency (vision & NLP)
traditional baseline
- 200‑300W per inference (GPU)
- constant retraining due to drift
- cloud efficiency declines sharply
- large models → 100ms+ lag
Quantum Tensor Networks
2× – 10× compression · 1.5× – 5× efficiency
μ‑State Variational
30% – 70% training reduction · ±1% accuracy stability
β‑Phase Stochastic
2× – 4× cost reduction · 30% – 60% drift reduction
optimized deployment – cloud & edge
SageMaker, Inferentia
Azure ML, ONNX
Google TPU, AI Platform
Jetson, Coral, OpenVINO
✔ TensorFlow, PyTorch, JAX, ARM, Intel Movidius, ≥1 TOPS edge validated.