How does AADC compare to TensorFlow for CPU performance?

For models requiring up to 1,000 inputs, AADC significantly outperforms TensorFlow and other Python-based frameworks on CPUs. This is particularly relevant for quantitative finance where model sizes are moderate but computational speed is critical. Both training and inference show significant speedups on CPUs.

Can I integrate custom neural network layers with my C++ quantitative models?

Yes, AADC allows seamless integration of machine learning with business analytics in C++. You can efficiently differentiate custom function definitions and create custom layers that integrate naturally with your existing codebase without framework overhead.

How can AAD help with Monte Carlo model calibration?

AADC enables Monte Carlo-based calibration of complex multi-asset models without requiring inflexible analytical approximations. You can calibrate models directly using simulation while efficiently computing gradients for optimization, avoiding the limitations of closed-form solutions.

What are the benefits for time series analysis in finance?

Automated custom design of neural networks for time series provides up to 3x better accuracy than cutting-edge methods, with several times lower training time due to AADC technology. Ideal for financial forecasting, trading signal generation, and risk modeling applications.

MatLogica | Machine Learning with AAD

The Performance Challenge: ML in Production Quantitative Systems

Financial institutions face a critical dilemma: Python-based ML frameworks are too slow for production, while rewriting models in C++ is expensive and time-consuming. AADC bridges this gap.

Why Traditional ML Frameworks Fall Short in Finance:
                                CPU Performance: TensorFlow/PyTorch optimized for GPUs, not production CPU systems
Integration Issues: Difficult to integrate with existing C++ analytics
Model Size Mismatch: Over-engineered for moderate-sized quant models (up to 1,000 inputs)

                                Deployment Complexity: Python dependencies in production environments
Training Overhead: Slower training cycles for iterative model development
No Business Logic Integration: Can't seamlessly mix ML with domain calculations

AADC vs TensorFlow: CPU Performance Comparison

ADBench benchmark results show AADC's significant advantage for quantitative finance applications

ADBench Performance Comparison: AADC vs TensorFlow vs PyTorch

Figure: AADC performance comparison on CPU showing significant advantages for models with up to 1,000 inputs

Key Performance Benefits:

10x+

Faster than TensorFlow on CPU for typical quant models

3x

More accurate time series forecasting vs cutting-edge methods

Several x

Faster training time for neural network development

Note: Python-based tools are catching up for very large problems (computer vision, linguistics) where performance is memory-bandwidth limited, but AADC excels in the sweet spot for quantitative finance.

Machine Learning Use Cases in Quantitative Finance

Where AADC's ML capabilities deliver the most value

State-of-the-Art Neural Networks for Time Series

Together with our research partners, we offer automated custom design of neural networks for time series problems. This approach often provides accuracies that are significantly better than standard neural network layers in TensorFlow or PyTorch.

Proven Results:

Up to 3x more accurate than available cutting-edge methods
Several times faster training due to AADC's AAD technology

Ideal for financial forecasting and trading signals
Risk modeling and scenario analysis

Research Paper: Learn about state-of-the-art neural network architectures for time series analysis in the paper by Prof. Roland Olsson and his team.

Download Paper Contact Us for Details

Integration Approaches

Choose the approach that fits your development workflow

Why AADC Excels at ML for Quantitative Finance

AADC vs Traditional ML Frameworks

For quantitative finance applications

Feature	TensorFlow/PyTorch	AADC	Advantage
CPU Performance (up to 1K inputs)	Baseline	10x+ faster	AADC
Training Speed	Baseline	Several times faster	AADC
Integration with C++ Analytics	Complex (language barriers)	Seamless	AADC
Production Deployment	Python runtime required	Native C++ binary	AADC
GPU Requirement	Needed for good performance	CPU-only	AADC
Custom Layer Development	Framework-specific APIs	Standard C++ code	AADC
Very Large Models (>10K inputs)	Better	Good	TensorFlow/PyTorch
Pre-trained Model Ecosystem	Extensive	Limited	TensorFlow/PyTorch

Note: AADC excels in the quantitative finance sweet spot (moderate model sizes, CPU deployment, integration with business logic), while TensorFlow/PyTorch are better for very large models and transfer learning scenarios.

Machine Learning with AAD

High-Performance Machine Learning for Quantitative Finance