Konstantinos Varsos

Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris et al.

Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representations involve integer constraints, for simulating the activations. Recent works convexly relax the integer constraints, improving performance, at the cost of soundness. Convex relaxations consider outputs that are unreachable by the original network. We study the worst case divergence between the original network and its convex relaxations; both qualitatively and quantitatively. The relaxations' space forms a lattice, where the top element corresponds to a full relaxation, with every neuron linearized. The bottom element corresponds to the original network. We provide analytical upper and lower bounds for the $\ell_\infty$-distance between the fully relaxed and original outputs. This distance grows exponentially, w.r.t. the network's depth, and linearly w.r.t. the input's radius. The misclassification probability exhibits a step-like behavior, w.r.t. input radius. Our results are supported by experiments on MNIST, Fashion MNIST and random networks.

Vasilios A. Siris, Adamantia Stamou, George D. Stamoulis et al.

The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the major contributor. This paper introduces a framework for designing AI inference incentives based on the users' valuation for inference quality and latency, together with their environmental consciousness, while accounting for the tradeoff between carbon emissions and the two QoE parameters. Our approach can accommodate different tradeoffs, that depend on the size and complexity of the AI models and the allocation of resources to serve inference requests. The incentives can be offered through a practical two-tier service subscription that offers users a discount in exchange for reduced carbon emissions. The discounted service option gives the AI provider the flexibility to serve some percentage of inference requests at a lower quality and higher latency during periods of high carbon intensity.

Vasilios A. Siris, Adamantia Stamou, George D. Stamoulis et al.

We investigate incentives for reducing the carbon emissions of video streaming that depend on the energy consumption of segments in the end-to-end video delivery path, the carbon intensity, and the user type, i.e., quality-sensitive and green or environmentally conscious users. The incentives can be offered through a practical 2-tier subscription model with a discount and carbon rewards, which gives providers the flexibility to reduce the quality for up to a maximum percentage of videos within a time period, such as one month. The key features of our approach are i) it is preferable to offer subscriptions where the reduced-quality tier is set one resolution level below the resolution required for maximum user satisfaction; ii) when a video is streamed from a local data center, the maximum percentage of videos streamed at a lower quality depends solely on the carbon intensity and the average intensity cap, whereas the incentives also depend on the users' level of environmental consciousness; iii) when a video can be streamed from a local or a remote data center with different carbon intensities, the maximum percentage of videos streamed at lower quality and the incentives depend on the relative carbon intensity and energy consumption at the data centers, and the additional network energy costs from the remote data center.