Deepity Released: A C++/CUDA Predictive Coding Library Outperforming Existing Solutions
By Mr.Xu Community Post
Published:
Summary:Deepity-HQ has released Deepity, a predictive coding (PC) library written in C++20, featuring hand-tuned AVX2/AVX-512 SIMD kernels, OpenBLAS/MKL for matrix operations, and a CUDA backend for optimization. Its Python bindings allow users to define networks similarly to PyTorch/Keras. Benchmarks on the MNIST dataset show Deepity outperforming existing solutions like JAX's jpc and ngc-learn. The library is currently in alpha, with the CPU path being the most mature and the CUDA backend still under
Deepity: A C++/CUDA Predictive Coding Library
Core Architecture and Features
Deepity is a from-scratch predictive coding (PC) engine built in C++20, incorporating the following technologies:
- High-Performance Kernel Optimization: Hand-written AVX2/AVX-512 SIMD kernels (using SLEEF for transcendental activations) ensure efficient computation.
- Accelerated Matrix Operations: Utilizes OpenBLAS/MKL for General Matrix Multiplication (GEMM).
- CUDA Backend Support: Integrates cuBLAS, CUTLASS, and CUDA graph capture for large-scale training optimization (currently under tuning).
Additionally, Deepity offers Python bindings (via nanobind), allowing users to define network structures similarly to PyTorch or Keras. Supported PCN variants include classic settling networks, Direct Kolen-Pollack feedback mechanisms, and a 'kitchen sink' network featuring µPC scaling, residual connections, incremental PC, error-based PC, and momentum settling.
Performance
In MNIST benchmark tests with a 784-512-512-10 Sigmoid network configuration (20 settling steps, batch size 250, 100 epochs, using AdamW optimizer), Deepity achieved 98.21% accuracy in 487.5 seconds. In comparison, JAX's jpc implementation scored 97.55% (531.4 seconds), and ngc-learn scored 98% (1707.7 seconds). For reference, a standard PyTorch backpropagation run on the same architecture achieved 97.51% (627.8 seconds).
Engineering Trade-offs and Challenges
- Performance Optimization vs. Generality: Deepity's optimizations through hand-written SIMD kernels and CUDA enhancements provide high performance but introduce dependencies on specific hardware platforms.
- CUDA Backend Maturity: While the CUDA backend builds and runs, large-scale training (e.g., ImageNet) is still being optimized, and the current im2col+cuBLAS approach is less efficient than cuDNN in some cases.
- Development and Maintenance: The project is currently maintained by a single developer, which may pose challenges for future scalability and stability.
Developer Recommendations
- Hardware Requirements: Due to Deepity's reliance on SIMD and CUDA, it is recommended to run it in environments with AVX2/AVX-512 and NVIDIA GPUs.
- Use Cases: Suitable for developers with a deep interest in predictive coding and a need for high-performance implementations.
- Contribution Opportunities: The project welcomes contributors with backgrounds in C++/CUDA performance optimization or build systems/DevOps.
Conclusion
Deepity's release brings a new high-performance implementation to the predictive coding field, particularly excelling in CPU optimization. However, the maturity of its CUDA backend and its development maintenance model warrant further observation. For developers looking to explore predictive coding, Deepity is a project worth considering.
— END —Source: Reddit r/MachineLearning (2026-10-26)
Tags: #Deepity #Predictive Coding #CUDA #High-Performance Computing #Open Source Library
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