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Updated
May 12, 2026
#
hardware-aware-training
Here are 3 public repositories matching this topic...
Differentiable stochastic-computing primitives for PyTorch: train neural networks natively SC-aware.
python machine-learning fpga deep-learning neural-network differentiable-computing pytorch embedded-systems stochastic quantization low-power bitstream stochastic-optimization neuromorphic-computing chip-design differentiable-optimization hardware-aware-training
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Jun 14, 2026 - Python
A PyTorch framework bridging the device-to-algorithm gap for Edge AI. MagNet (Magnetic + Neural Network) simulates SOT-MTJ hardware constraints (quantization, read noise) to enable Quantization-Aware Training (QAT) for spintronic neural networks.
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Mar 9, 2026 - Python
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