High level GPU simulations of low level device characteristics in ML algorithms
Project description
Welcome to LowPy (Pre-release)!
LowPy is a high level GPU simulator of low level device characteristics in machine algorithms. It seeks to streamline the investigation process when considering memristive and other novel devices for implementing a machine learning algorithm in hardware. By using the familiar Keras syntax, it will be second nature to write GPU-optimized code to push your algorithm to its limits.
Features
The aim is to focus first on the algorithms most published on in the field of neuromorphic computing, for both static and time series datasets.
Datasets
- MNIST
Algorithms
- Single Layer Perceptron (SLP)
- Multi-Layer Perceprton (MLP)
Activation Functions
- Sigmoid
Optimization Functions
- Stochastic Gradient Descent (SGD)
- SGD with Momentum
Initialization Distributions
- Uniform
- Normal
Device Characteristics
- Write Variability
Requirements
The following are required to use LowPy:
- GPU: NVIDIA
- OS: Linux (should work on Windows, not tested)
- Python 3.0 or newer
- PyCUDA
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