27th International Workshop on Radiation Imaging Detectors

Memristive tabular variational autoencoder for compression of analog data in high energy physics

Jul 1, 2026, 5:40 PM

20m

Ghent, Belgium

Poster presentation only Front end electronics and readout Poster session 2

Speaker

Tae Min Hong (University of Pittsburgh (US))

Description

We present an implementation of edge AI to compress data on an in-memory analog content-addressable memory (ACAM) device. A variational autoencoder is trained on a simulated sample of energy measurements from incident high-energy electrons on a generic three-layer scintillator-based calorimeter. The encoding part is distilled into tabular format by regressing the latent space variables using decision trees, which is then programmed on a memristor-based ACAM. In real-time, the ACAM compresses 48 continuously valued incoming energies measured by the calorimeter sensors into the latent space, achieving a compression factor of 12x, which is transmitted off-detector for decompression. The talk is based on our preprint (arXiv:2602.15990).

Presentation materials

TMHong20260701_iWorid_MemristiveTabularAutoencoder.pdf