PySAMACT: Python API for PoC of the SAMACT Hardware Core IP
SAMACTは、メイビスデザイン株式会社が開発・保有する、オンチップ学習に対応したエンドポイント向けAI IPです。
本ライブラリは、SAMACTのハードウェア実装と同等の数値仕様および演算仕様を再現したPythonライブラリであり、主にPoC(概念実証)用途での利用を想定しています。
SAMACTの詳細については、当社Webサイトをご参照ください。
概要 (Overview)
本ライブラリは、以下を目的として開発されています。
- ハードウェア(RTL / 専用回路)実装向けニューラルネットワークの
事前評価を Python 上で行う - 量子化・固定小数点演算など、HW特有の挙動を再現
⚠️ 本ライブラリは 高速な学習・高性能推論用途を目的としていません。
特徴 (Features)
- ハードウェアと対応した演算仕様
- 固定小数点 / ビット幅制約
- Saturation / Clipping
- Python / NumPy ベースの実装
- PoCでの利用を想定したシンプルなAPI
非対象 (Non-Goals)
以下は明示的にスコープ外です。
- GPU / 高速化最適化
- PyTorch / TensorFlow 互換 API の完全再現
- 自動微分
インストール (Installation)
pip install samact
想定ユースケース (Use Cases)
- 機械学習プロジェクトのPoC
- RTL 実装前の数値シミュレーション
API仕様書 (API Document)
API仕様書は下記をご覧ください。
https://maviss-design.github.io/SAMACT/
制限事項 (Limitations)
- 実行速度は最適化されていません
- ソースコードはPyPIで公開されており、githubでは公開されていません。
ライセンス (License)
AGPLv3です。
詳細は LICENSE を参照してください。
注意事項
本ライブラリは 研究・検証用途を目的としています。
商用品質・量産用途への直接利用は想定していません。
英語
PySAMACT: Python API for PoC of the SAMACT Hardware Core IP
SAMACT is an AI IP for endpoint devices with on-chip learning capability, developed and owned by Maviss Design Inc.
This library is a Python implementation that reproduces the numerical specifications and computational behavior equivalent to the SAMACT hardware implementation, and is primarily intended for Proof of Concept (PoC) use.
For more details on SAMACT, please refer to our website
Overview
This library is developed for the following purposes:
- To perform pre-evaluation of neural networks on Python before hardware (RTL / dedicated circuit) implementation
- To reproduce hardware-specific behavior such as quantization and fixed-point arithmetic
⚠️ This library is not intended for high-speed training or high-performance inference.
Features
- Computation specifications aligned with hardware
- Fixed-point arithmetic / bit-width constraints
- Saturation / Clipping
- Python / NumPy-based implementation
- Simple API designed for PoC use
Non-Goals
The following are explicitly out of scope:
- GPU acceleration / performance optimization
- Full compatibility with PyTorch / TensorFlow APIs
- Automatic differentiation
Installation
pip install samact
Use Cases
- PoC for machine learning projects
- Numerical simulation prior to RTL implementation
API Documentation
Please refer to the following for the API documentation:
https://maviss-design.github.io/SAMACT/
Limitations
- Execution speed is not optimized
- Source code is distributed via PyPI and is not publicly available on GitHub
License
AGPLv3.
See LICENSE.
Notes
This library is intended for research and validation purposes.
It is not designed for direct use in production-quality or mass-production applications.
Metadata
Release files for samact 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| samact-1.0.0.tar.gz | 83.1 kB | Without provenance | Details for samact-1.0.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| samact-1.0.0-py3-none-any.whl | Python 3 | none | any | Details for samact-1.0.0-py3-none-any.whl |
Total release size: 207.4 kB
Release files / samact-1.0.0.tar.gz
| Download URL | samact-1.0.0.tar.gz |
|---|---|
| Size | 83.1 kB |
| Tags | Source |
|
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.9
|
Release files / samact-1.0.0-py3-none-any.whl
| Download URL | samact-1.0.0-py3-none-any.whl |
|---|---|
| Size | 124.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.9
|
Security for samact 1.0.0
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No provenance found
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