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DOE OSTI2022

Distributed Finite-Time Termination for Consensus Algorithm in Switching Topologies

Here, in this article, we present a finite-time stopping criterion for consensus algorithms in networks with dynamic communication topology. Prior state of the art has established convergence to the consensus value; however, the asymptotic convergence of these algorithms poses a challenge in practical settings where the response from agents is required in finite time. To this end, we propose a maximum-minimum protocol that propagates the global maximum and minimum values of agent states (while running the consensus algorithm) in the network. This article focuses on establishing that the global maximum and minimum values are strictly monotonic even for a dynamic topology, and they can be used to distributively ascertain the closeness to convergence in finite time. We rigorously show that each node can have access to the global maximum and minimum by running the proposed maximum-minimum protocol to realize a finite-time stopping criterion for the otherwise asymptotic consensus algorithm. The practical utility of the algorithm is illustrated through experiments where each agent is instantiated by a NodeJS socket.io server.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

SwitchX : Gmin-Gmax Switching for Energy-efficient and Robust Implementation of Binarized Neural Networks on ReRAM Xbars

Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of the synapses. We propose SwitchX mapping of BNN weights onto ReRAM crossbars such that the impact of crossbar non-idealities, that lead to degradation in computational accuracy, are minimized. Essentially, SwitchX maps the binary weights in such a manner that a crossbar instance comprises of more HRS than LRS synapses. We find BNNs mapped onto crossbars with SwitchX to exhibit better robustness against adversarial attacks than the standard crossbar mapped BNNs, the baseline. Finally, we combine SwitchX with state-aware training (that further increases the feasibility of HRS states during weight mapping) to boost the robustness of a BNN on hardware. We find that this approach yields stronger defense against adversarial attacks than adversarial training, a state-of the-art software defense. We perform experiments on a VGG16 BNN with benchmark datasets (CIFAR-10, CIFAR-100 and TinyImagenet) and use Fast Gradient Sign Method (ϵ = 0.05 to 0.3) and Projected Gradient Descent (ϵ = $\frac{2}{255}$ to $\frac{32}{255}$, α = $\frac{2}{255}$) adversarial attacks. We show that SwitchX combined with state-aware training can yield upto ~35% improvements in clean accuracy and ~6–16% in adversarial accuracies against conventional BNNs. Furthermore, an important by-product of SwitchX mapping is increased crossbar power savings, owing to an increased proportion of HRS synapses, which is furthered with state-aware training. We obtain upto ~21–22% savings in crossbar power consumption for state-aware trained BNN mapped via SwitchX on 16 × 16 and 32 × 32 crossbars using the CIFAR-10 and CIFAR-100 datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Structure of the Diphtheria Toxin at Acidic pH: Implications for the Conformational Switching of the Translocation Domain

Diphtheria toxin, an exotoxin secreted by Corynebacterium that causes disease in humans by inhibiting protein synthesis, enters the cell via receptor-mediated endocytosis. The subsequent endosomal acidification triggers a series of conformational changes, resulting in the refolding and membrane insertion of the translocation (T-)domain and ultimately leading to the translocation of the catalytic domain into the cytoplasm. Here, we use X-ray crystallography along with circular dichroism and fluorescence spectroscopy to gain insight into the mechanism of the early stages of pH-dependent conformational transition. For the first time, we present the high-resolution structure of the diphtheria toxin at a mildly acidic pH (5–6) and compare it to the structure at neutral pH (7). We demonstrate that neither catalytic nor receptor-binding domains change their structure upon this acidification, while the T-domain undergoes a conformational change that results in the unfolding of the TH2–3 helices. Surprisingly, the TH1 helix maintains its conformation in the crystal of the full-length toxin even at pH 5. This contrasts with the evidence from the new and previously published data, obtained by spectroscopic measurements and molecular dynamics computer simulations, which indicate the refolding of TH1 upon the acidification of the isolated T-domain. The overall results imply that the membrane interactions of the T-domain are critical in ensuring the proper conformational changes required for the preparation of the diphtheria toxin for the cellular entry.

59 BASIC BIOLOGICAL SCIENCES↗