HYPERQ
Sustainable Machine Learning
Faster Training. Lower Costs. Better Planet.
POWERING SUSTAINABLE
AI TRAINING
HyperQ is leading the way in sustainable AI training through our state of the art quantum-inspired optimization techniques that reduce energy consumption without compromising model performance. Our mission is to make AI training more environmentally friendly, cost-effective, and accessible for the next generation of machine learning innovators.
50%
Reduced Memory Footprint with FP16 Moment Storage
5
Real Time and Adaptive Monitoring Systems
3
Layers of Gradient Protection
OUR PROCESS
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The HyperQ Optimizer
Our flagship product, the HyperQ Optimizer, is designed to enable companies to achieve a significant reduction in overall energy costs and carbon footprint while maintaining industry-standard accuracy for image classification CNNs. It has proven to be 7-13% more energy efficient than AdamW on the CIFAR10 dataset with ResNet18, ResNet34, and ResNet50 with high target accuracies. In addition, it has scaled seamlessly and benchmark data shows it is 18-31% more energy efficient than AdamW on ResNet50 trained on the ImageNET100 dataset, a subset of the gold-standard ImageNET-1k dataset. This was achieved with an impressive target accuracy of 80% from scratch (no pretrained weights). It is currently scaling to Vision Transformers Models, benchmarking against state of the art optimizers (SGD with momentum, AdamW).
OUR RESULTS
Rigorous Performance Validation Benchmark Methodology (HyperQ is QIO-Adaptive)
Our benchmarks employ industry-standard practices to ensure fair and reproducible comparisons.
Baseline Comparison: HyperQ (QIO-Adaptive) is benchmarked against optimally-tuned SGD with Momentum and AdamW optimizers
Hardware Monitoring: Real-time GPU power consumption measured via NVIDIA-SMI at 100ms intervals
Statistical Rigor: Each experiment repeated with 5 randomized seeds for statistical significance
Mixed Precision: All optimizers tested with PyTorch Automatic Mixed Precision (AMP) for fair comparison
Target-Based Evaluation: Performance measured by time and energy to reach specific accuracy targets
Energy Efficiency Comparison for ResNet 50 x ImageNET100
for 80% Target Accuracy From Scratch
OUR TEAM
