Machine Learning

High-Performance Machine Learning for Quantitative Finance

Integrate custom neural networks seamlessly into your C++ analytics with AADC. Outperform TensorFlow on CPU for models up to 1,000 inputs while achieving faster training and inference for production quantitative systems.

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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

ADBench Performance Comparison: AADC vs TensorFlow vs PyTorch

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
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.

Research & Publications

State-of-the-art neural network research from MatLogica and partners

ARN: Automatic Synthesis of Neurons for Recurrent Neural Nets

Novel ARN neurons achieve up to 3x better accuracy than LSTMs on time series tasks through automatic synthesis of recurrent architectures with skip connections and novel activation functions.

View Paper

Automatic Synthesis of Recurrent Neurons for CNC Imitation Learning

IEEE paper exploring innovative automatic synthesis of recurrent neurons to enable imitation learning directly from expert CNC machine operators.

View Paper

Deep Learning for Oil Well Monitoring and Anomaly Detection

IEEE Computer Society publication on advanced computer vision techniques for monitoring oil wells using deep learning.

View Paper

Integration Approaches

Choose the approach that fits your development workflow

Approach 1

Standalone ML Models

Build neural networks in C++ with AADC for pure ML workloads

  • Replace TensorFlow/PyTorch for CPU deployment
  • 10x+ faster training and inference
  • No Python runtime dependencies
  • Standard neural network layers available
Approach 2

Hybrid Analytics + ML

Seamlessly interleave ML with quantitative business logic

  • ML layers embedded in pricing/risk models
  • Single differentiation pass through entire pipeline
  • Domain calculations + ML predictions unified
  • Natural C++ code integration

Why AADC Excels at ML for Quantitative Finance

Optimized for Model Size

TensorFlow and PyTorch are designed for massive models (millions of parameters). Quantitative finance typically needs models with hundreds to thousands of inputs—AADC's sweet spot.

CPU-First Architecture

Most ML frameworks optimize for GPUs. AADC is built for CPU performance, which is where production quant systems run. No GPU infrastructure needed.

Zero Framework Overhead

No graph building overhead, no Python interpreter, no framework abstractions. Direct compilation to optimized machine code with automatic differentiation.

Natural C++ Integration

ML layers integrate seamlessly with your existing C++ analytics. No language barriers, no serialization overhead, no deployment complexity.

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.

Ready to Accelerate Your ML Workflows?

See how AADC can deliver 10x+ performance improvements for your machine learning applications in quantitative finance.

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