The paper investigates how temperature affects analog deep neural network (DNN) inference, focusing on both stochastic and systematic non‑idealities in analog hardware. Experiments show that temperature‑induced performance loss is mainly driven by systematic errors rather than random noise. The study evaluates various mitigation techniques, finding that noise‑aware training and temperature‑aware calibration—especially hardware‑in‑the‑loop training—best preserve inference accuracy across different thermal conditions.
By Niklas Summ, Xiao Wang, Hendrik Borras, Bernhard Klein, Holger Fr\"oning
arXiv:2510. 23389v2 Announce Type: replace-cross Abstract: The behaviour of neural network components must be proven correct before deployment in safety-critical systems.
By Edoardo Manino, Bruno Farias, Rafael S\'a Menezes, Fedor Shmarov, Lucas C. Cordeiro
arXiv:2603. 23878v3 Announce Type: replace-cross Abstract: The parameterized CROWN analysis, a.
By Henry LeCates, Haoze Wu
arXiv:2609.25962v1 Announce Type: new
Abstract: Neural network verification has become a key tool for providing formal guarantees on the behaviour of neural networks. However, many verification probl...
By Annelot Bosman, Minghao Liu, Marta Kwiatkowska, Holger Hoos, Jan van Rijn
PANDA is a scalable system that uses zero‑knowledge proofs to certify the robustness and fairness of neural networks without revealing their private parameters. Built on the CROWN robustness framework, PANDA introduces a novel algorithm for proving linear relaxation bounds on non‑linear activation layers, producing lightweight proofs. The system can generate proofs for networks with over 2.9 million parameters in just five minutes and verify them in ten seconds, scaling polynomially with network size and enabling verification of models four orders of magnitude larger than prior ZKP‑based approaches.
By Youwei Zhong, Ben Merbaum, Timos Antonopoulos, Ning Luo, Charalampos Papamanthou, Katerina Sotiraki, Ruzica Piskac
The paper introduces a new neural network architecture that represents mixed‑monotone functions as the difference of two monotone subnetworks, enabling tight output enclosures via only two forward passes of opposite corners of an input interval. The enclosure size is a closed‑form function of the network weights, allowing direct minimization during training. Experiments on regression and dynamical‑system benchmarks show that the trained networks achieve enclosure sizes within 0.5% of a proven lower bound while maintaining prediction accuracy, and the method is significantly faster and often tighter than existing techniques such as CROWN, α‑CROWN, and zonotope propagation.
By Jonas Fingerhut, Lukas Koller, Matthias Althoff