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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.15990 |
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| _version_ | 1866918343680720896 |
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| author | Gupta, Rajat Elangovan, Yuvaraj Hong, Tae Min Ignowski, James Moon, John Natarajan, Aishwarya Roche, Stephen Buonanno, Luca |
| author_facet | Gupta, Rajat Elangovan, Yuvaraj Hong, Tae Min Ignowski, James Moon, John Natarajan, Aishwarya Roche, Stephen Buonanno, Luca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15990 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Memristive tabular variational autoencoder for compression of analog data in high energy physics Gupta, Rajat Elangovan, Yuvaraj Hong, Tae Min Ignowski, James Moon, John Natarajan, Aishwarya Roche, Stephen Buonanno, Luca Instrumentation and Detectors High Energy Physics - Experiment Data Analysis, Statistics and Probability 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. |
| title | Memristive tabular variational autoencoder for compression of analog data in high energy physics |
| topic | Instrumentation and Detectors High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2602.15990 |