GPU-accelerated Neural Network layers using Approximate Multiplications for PyTorch
The SubXPAT approximate logic synthesis framework
Code for the paper "Combining Gradients and Probabilities for Heterogeneours Approximation of Neural Networks"
Pytorch implementation of TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators
Repository accompanying the paper "Fixed-Posit: A Floating-Point Representation for Error-Resilient Applications" published in IEEE Transactions on Circuits and Systems II
Experimental framework for approximate computing on CNNs: pruning, quantization, sparsity analysis on ResNet-18 and MobileNetV2.
A radix 4 booth multiplier that trades off accuracy for speed and area considerations
A Comprehensive Guide to Pytest Approx for Accurate Numeric Testing
Takes a mathematical function, chooses a “nice” nearby reference point, approximates the function at a user-defined value, and compares approximation vs actual value. The optionally visualises the function over a range.
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