# Reliable edge machine learning hardware for scientific applications

arXiv: https://arxiv.org/abs/2406.19522 · HTML: https://mlsanity.com/paper/2406.19522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [PyHessian: Neural Networks Through the Lens of the Hessian](https://mlsanity.com/paper/1912.07145/) (2019)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Robust Learning with Jacobian Regularization](https://mlsanity.com/paper/1908.02729/) (2019)
- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://mlsanity.com/paper/2006.10159/) (2020)
- [A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness](https://mlsanity.com/paper/2106.09129/) (2021)
- [Taxonomizing local versus global structure in neural network loss landscapes](https://mlsanity.com/paper/2107.11228/) (2021)
- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC](https://mlsanity.com/paper/2105.01683/) (2021)
- [Assessing Intelligence in Artificial Neural Networks](https://mlsanity.com/paper/2006.02909/) (2020)
- [Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications](https://mlsanity.com/paper/2403.08980/) (2024)

## Built on by (most cited first, 2 of 2)

- [End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml](https://mlsanity.com/paper/2501.14663/) (2025)
- [Design Rules for Extreme-Edge Scientific Computing on AI Engines](https://mlsanity.com/paper/2604.19106/) (2026)

## Abstract (arXiv)

Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery.