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 performance result of the ACAM, obtained using the Structural Simulation Toolkit, the SST open source framework, gives a latency value of 24 ns and a throughput of 330M compressions per second, i.e., 3 ns between successive inputs, and an average energy consumption of 4.1 nJ per compression.
@article{arxiv.2602.15990,
title = {Memristive tabular variational autoencoder for compression of analog data in high energy physics},
author = {Rajat Gupta and Yuvaraj Elangovan and Tae Min Hong and James Ignowski and John Moon and Aishwarya Natarajan and Stephen Roche and Luca Buonanno},
journal= {arXiv preprint arXiv:2602.15990},
year = {2026}
}