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

TL;DR

This paper introduces a memristive tabular variational autoencoder for real-time compression of analog data in high energy physics, achieving high speed and low energy consumption on in-memory devices.

Contribution

It presents a novel in-memory compression method using memristor-based ACAM and decision trees to efficiently encode high-energy physics data in real-time.

Findings

12x data compression factor achieved

24 ns latency for compression

330 million compressions per second throughput

Abstract

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.