Zero-shot quantization (ZSQ) using synthetic data is a key approach for post-training quantization (PTQ) under privacy and security constraints. However, existing data generation methods often struggle to effectively generate data suitable for hardware-friendly quantization, where all model layers are quantized. We analyze existing data generation methods based on batch normalization (BN) matching and identify several gaps between synthetic and real data: 1) Current generation algorithms do not optimize the entire synthetic dataset simultaneously; 2) Data augmentations applied during training are often overlooked; and 3) A distribution shift occurs in the final model layers due to the absence of BN in those layers. These gaps negatively impact ZSQ performance, particularly in hardware-friendly quantization scenarios. In this work, we propose Data Generation for Hardware-Friendly Quantization (DGH), a novel method that addresses these gaps. DGH jointly optimizes all generated images, regardless of the image set size or GPU memory constraints. To address data augmentation mismatches, DGH includes a preprocessing stage that mimics the augmentation process and enhances image quality by incorporating natural image priors. Finally, we propose a new distribution-stretching loss that aligns the support of the feature map distribution between real and synthetic data. This loss is applied to the model's output and can be adapted to various tasks. DGH demonstrates significant improvements in quantization performance across multiple tasks, achieving up to a 30% increase in accuracy for hardware-friendly ZSQ in both classification and object detection, often performing on par with real data.

In this paper, we investigate problems of communication over physically degraded, state-dependent broadcast channels (BCs) with cooperating decoders. Two different setups are considered, and their capacity regions are characterized. First, we study a setting in which one decoder can use a finite capacity link to send the other decoder information regarding the messages or the channel states. In this scenario, we analyze two cases: one, where noncausal state information, is available to the encoder and the strong decoder, and the other, where state information, is available only to the encoder in a causal manner. Second, we examine a setting in which the cooperation between the decoders is limited to taking place before the outputs of the channel are given. In this case, one decoder, which is informed of the state sequence noncausally, can cooperate only to send the other decoder rate-limited information about the state sequence. The proofs of the capacity regions introduce a new idea of coding for channels with cooperation between different users, where we exploit the link between the decoders for multiple binnings. Finally, we discuss the optimality of using rate-splitting techniques when coding for cooperative BCs. In particular, we show that rate splitting is not necessarily optimal when coding for cooperative BCs by solving an example in which our method of coding outperforms rate splitting.

The growing interest in action-dependent channels motivates us to extend the study of action-dependent settings, which until now focused on point-to-point models, to multiple-access channels (MACs). In this paper, we consider a two-user, state-dependent MAC, in which one of the encoders, called the informed (cognitive) encoder, is allowed to take an action that affects the formation of the channel states. Two independent messages are to be sent through the channel: (1) a common message known to both encoders and (2) a private message known only to the informed encoder. In addition, the informed encoder has access to the sequence of channel states in a noncausal manner. Our framework generalizes the previously evaluated settings of state-dependent point-to-point channels with actions and MACs with common messages. We derive a single letter characterization of the capacity region for this setting. Using this general result, we obtain and compute the capacity region for the Gaussian action-dependent MAC. The special methods used in solving the Gaussian case are then applied to obtain the capacity of the Gaussian action-dependent point-to-point channel, a problem left open and solved now. Finally, we establish some dualities between action-dependent channel coding and source coding problems. In particular, we obtain a duality equivalence between the considered MAC setting and the rate distortion model known as successive refinement with actions. This is done by developing a set of simple duality principles that enables us to successfully evaluate the outcome of one problem given the outcome of the other.

We consider a two-user multiple-access channel, where one of the encoders is allowed to take an action that effects the formation of the channel states. The states are know non-causally to the encoder. Two independent messages are sent: a common message and a private message transmitted by the informed encoder. We find precise characterizations of the capacity region. Our framework takes account of previously evaluated settings regarding point-to-point channels with actions and multiple-access channels with common messages. We obtain the capacity of the Gaussian action-dependent point-to-point channel.