m4urin/quantized-liquid-state-machines
A Liquid State Machine using quantized neurons that are operating on lower-bit representations and fixed point computations. It provides a next step towards the implementation of efficient accelerators that can be used in the field of neuromorphic computing.
This project helps neuromorphic computing researchers and hardware engineers design and evaluate more efficient brain-inspired AI systems. It takes in parameters like neuron bit representation, liquid size, and time series encoding, then outputs measures of prediction accuracy and computational efficiency for a given Liquid State Machine configuration. The primary users are those focused on optimizing the power and speed of neuromorphic hardware.
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Use this if you are a neuromorphic computing researcher or hardware designer exploring the trade-offs between accuracy and efficiency in quantized Liquid State Machines.
Not ideal if you are looking for a general-purpose machine learning library or a tool for applying pre-built models to real-world data.
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Jupyter Notebook
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Apache-2.0
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Last pushed
Apr 08, 2024
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