mirror of
https://github.com/hanzoai/papers.git
synced 2026-10-29 07:34:31 +00:00
zeekay
0d1d121451
Fix shared/ include paths to ../shared/ for 55 subdirectory papers so
that hanzocover.sty and lstlang.tex resolve when building from each
paper's own directory (the build pattern used by latexmk per paper).
Build fixes:
- hanzo-operative/sections/03-screen-understanding-pipeline.tex: move
\COMMENT from after \FOR{...} onto the \STATE line so the algorithmic
env doesn't mis-parse.
- hanzo-network-architecture (abstract + sections 04, 12, 13): escape
unicode mu (U+03BC) as $\mu$ for pdflatex.
- ntt-transform/sections/08-benchmark-results.tex: same mu escape.
All 55 Tier-2/3 papers now build clean with latexmk -pdf.
64 lines
2.5 KiB
TeX
64 lines
2.5 KiB
TeX
\documentclass[11pt,twocolumn]{article}
|
|
\usepackage{../shared/hanzocover}
|
|
\usepackage[utf8]{inputenc}
|
|
\usepackage{amsmath,amssymb,amsthm}
|
|
\usepackage{graphicx}
|
|
\usepackage{booktabs}
|
|
\usepackage{algorithm}
|
|
\usepackage{algorithmic}
|
|
\usepackage{hyperref}
|
|
\usepackage{xcolor}
|
|
\usepackage{listings}
|
|
\input{../shared/lstlang}
|
|
\usepackage{tikz}
|
|
\usetikzlibrary{shapes,arrows,positioning,fit,backgrounds}
|
|
|
|
\definecolor{codegreen}{rgb}{0,0.6,0}
|
|
\definecolor{codegray}{rgb}{0.5,0.5,0.5}
|
|
\definecolor{codepurple}{rgb}{0.58,0,0.82}
|
|
\definecolor{backcolour}{rgb}{0.95,0.95,0.92}
|
|
|
|
\lstdefinestyle{mystyle}{
|
|
backgroundcolor=\color{backcolour},
|
|
commentstyle=\color{codegreen},
|
|
keywordstyle=\color{codepurple},
|
|
numberstyle=\tiny\color{codegray},
|
|
stringstyle=\color{codegreen},
|
|
basicstyle=\ttfamily\footnotesize,
|
|
breakatwhitespace=false,
|
|
breaklines=true,
|
|
captionpos=b,
|
|
keepspaces=true,
|
|
numbers=left,
|
|
numbersep=5pt,
|
|
showspaces=false,
|
|
showstringspaces=false,
|
|
showtabs=false,
|
|
tabsize=2
|
|
}
|
|
\lstset{style=mystyle}
|
|
|
|
\title{Hanzo ML: A Production ML Framework for Model Serving, A/B Testing, and Feature Stores}
|
|
\author{Antje Worring, Zach Kelling\thanks{[email protected]} \\ \textit{Hanzo Industries \quad Lux Industries \quad Zoo Labs Foundation} \\ \texttt{[email protected]}}
|
|
\date{July 2022}
|
|
|
|
\begin{document}
|
|
|
|
\hanzocoverpage
|
|
|
|
\begin{abstract}
|
|
We present Hanzo ML, a Rust-based machine learning framework optimized for serverless deployment and fully homomorphic encryption (FHE) compatibility. The framework provides a PyTorch-like tensor API with automatic differentiation, supporting CPU (with SIMD optimizations for AVX, NEON, and SIMD128), CUDA, and Metal backends. Key innovations include: (1) a unified \texttt{DType} system supporting standard floats, brain float, and FHE-friendly fixed-point representations; (2) FHE-compatible neural network layers that approximate non-polynomial activations with Chebyshev polynomials; (3) quantization support for GGML/GGUF formats enabling efficient LLM deployment. Benchmarks demonstrate 3.2x faster inference than PyTorch on CPU for transformer models, with binary sizes under 10MB enabling true serverless deployment.
|
|
\end{abstract}
|
|
|
|
\input{sections/01-introduction}
|
|
\input{sections/02-background}
|
|
\input{sections/03-architecture}
|
|
\input{sections/04-fhe-compatible-layers}
|
|
\input{sections/05-quantization-support}
|
|
\input{sections/06-evaluation}
|
|
\input{sections/07-transformers-library}
|
|
\input{sections/08-discussion}
|
|
\input{sections/99-conclusion}
|
|
|
|
\end{document}
|