NeuroX: A PyTorch-based neuromorphic computing simulation framework, from device physics to AI models.
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Oct 9, 2026 - Python
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hardware-aware-training
Here are 7 public repositories matching this topic...
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
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
Hardware-Conscious Software Training (HCST) and circuit-derived modeling for fully analog ReRAM inference, including finite-gain and fixed-offset effects.
memristor analog-computing reram computing-in-memory neural-network-accelerator in-memory-computing hardware-aware-training crossbar-array
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Oct 7, 2026 - Python
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May 12, 2026
Hardware-aware S4D state-space models on simulated analog memristor crossbars: a reproducible neuromorphic in-memory computing co-design study (PyTorch, CPU-only).
machine-learning pytorch memristor neuromorphic-computing state-space-models analog-computing in-memory-computing hardware-aware-training
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Jul 20, 2026 - Python
Sensitivity-aware placement of GPT-2 onto aging analog in-memory computing (AIMC) tiles: hardware-aware fine-tuning, noise-sensitivity profiling, tile-aging simulation, and lifetime evaluation with IBM AIHWKit.
pytorch gpt-2 llm-inference wikitext-103 compute-in-memory aihwkit hardware-aware-training analog-in-memory-computing
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Oct 26, 2026 - Python
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