no code implementations • 24 Feb 2023 • Mohanad Odema, James Ferlez, Yasser Shoukry, Mohammad Abdullah Al Faruque
Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints.
no code implementations • 13 Feb 2023 • Mohanad Odema, James Ferlez, Goli Vaisi, Yasser Shoukry, Mohammad Abdullah Al Faruque
To mitigate the high energy demand of Neural Network (NN) based Autonomous Driving Systems (ADSs), we consider the problem of offloading NN controllers from the ADS to nearby edge-computing infrastructure, but in such a way that formal vehicle safety properties are guaranteed.
no code implementations • 20 Sep 2022 • James Ferlez, Yasser Shoukry
In this paper, we consider the computational complexity of bounding the reachable set of a Linear Time-Invariant (LTI) system controlled by a Rectified Linear Unit (ReLU) Two-Level Lattice (TLL) Neural Network (NN) controller.
no code implementations • 17 Nov 2021 • James Ferlez, Haitham Khedr, Yasser Shoukry
In this paper, we present the tool Fast Box Analysis of Two-Level Lattice Neural Networks (Fast BATLLNN) as a fast verifier of box-like output constraints for Two-Level Lattice (TLL) Neural Networks (NNs).
no code implementations • 21 Sep 2021 • James Ferlez, Yasser Shoukry
In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture (number of layers and number of neurons per layer) with the assurance that it is sufficiently parametrized to control a nonlinear system; i. e. control the system to satisfy a given formal specification.
no code implementations • 6 Apr 2021 • Ulices Santa Cruz, James Ferlez, Yasser Shoukry
In this paper, we consider the problem of repairing a data-trained Rectified Linear Unit (ReLU) Neural Network (NN) controller for a discrete-time, input-affine system.
no code implementations • 22 Dec 2020 • James Ferlez, Yasser Shoukry
Specifically, we show that for two different NN architectures -- shallow NNs and Two-Level Lattice (TLL) NNs -- the verification problem with (convex) polytopic constraints is polynomial in the number of neurons in the NN to be verified, when all other aspects of the verification problem held fixed.
1 code implementation • 18 Jun 2020 • Haitham Khedr, James Ferlez, Yasser Shoukry
However, unique in our approach is the way we use a convex solver not only as a linear feasibility checker, but also as a means of penalizing the amount of relaxation allowed in solutions.
no code implementations • 16 Jun 2020 • James Ferlez, Mahmoud Elnaggar, Yasser Shoukry, Cody Fleming
In this paper, we consider the problem of creating a safe-by-design Rectified Linear Unit (ReLU) Neural Network (NN), which, when composed with an arbitrary control NN, makes the composition provably safe.
no code implementations • 20 Apr 2020 • James Ferlez, Xiaowu Sun, Yasser Shoukry
In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture (number of layers and number of neurons per layer) with the guarantee that it is sufficiently parametrized to control a nonlinear system.
no code implementations • 5 Nov 2019 • James Ferlez, Yasser Shoukry
In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture that is sufficient to implement the optimal Model Predictive Control (MPC) strategy for an LTI system with quadratic cost.