[ASPLOS 2019] PUMA-simulator provides a detailed simulation model of a dataflow architecture built with NVM (non-volatile memory), and runs ML models compiled using the puma compiler.
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Updated
Apr 17, 2023 - Python
[ASPLOS 2019] PUMA-simulator provides a detailed simulation model of a dataflow architecture built with NVM (non-volatile memory), and runs ML models compiled using the puma compiler.
[Nature Machine Intelligence 2023] "Echo state graph neural networks with analogue random resistive memory arrays."
Behavioral compute-SNR analysis and website for voltage-driven resistive crossbar IMCs
Device-to-algorithm neuromorphic: train spiking neural nets in snnTorch, then deploy on a simulated memristor crossbar with measured SnS2 device non-idealities.
Hardware-aware S4D state-space models on simulated analog memristor crossbars: a reproducible neuromorphic in-memory computing co-design study (PyTorch, CPU-only).
Analog Robustness CI — will your AI model survive analog hardware? Break-even economics + noise/drift/ADC robustness simulation for AIMC and photonic accelerators.
Differential memristive crossbar with a hardware-friendly in-situ (Manhattan/sign-rule) learning rule, tested on parity-3 — the calibrated in-memory-compute baseline of the physical-learning-substrates portfolio. Verdict #1: PASS, learns parity-3 at SNR ~24.5 (half co-located: physics activations, off-array error sign, physical-pulse increment).
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