papers/hanzo-ml-framework/sections/01-introduction.tex

\section{Introduction}


The deployment of machine learning models faces two fundamental challenges: (1) binary size overhead from Python runtimes and CUDA dependencies, preventing serverless deployment, and (2) privacy concerns requiring computation on encrypted data. Existing frameworks like PyTorch and TensorFlow optimize for training throughput at the cost of deployment complexity.

Hanzo ML addresses these challenges through a Rust-native implementation that:
\begin{enumerate}
    \item Produces small, statically-linked binaries suitable for serverless functions
    \item Supports FHE-compatible operations for privacy-preserving inference
    \item Provides SIMD-optimized kernels across CPU architectures
    \item Enables efficient quantized inference for large language models
\end{enumerate}

\paragraph{Contributions.} This paper makes the following contributions:
\begin{itemize}
    \item A tensor implementation with automatic differentiation and lazy evaluation, achieving 3.2x faster inference than PyTorch on CPU
    \item An FHE-compatible layer library using polynomial approximations, enabling neural network inference on encrypted data
    \item SIMD kernels for AVX-512, AVX2, NEON, and SIMD128, with automatic runtime dispatch
    \item Quantization support for k-quants formats (Q4\_K, Q5\_K, Q6\_K) with 2-bit precision loss
\end{itemize}