Materials informatics (MI), which emerges from the integration of materials science and data science, is expected to greatly streamline material discovery and development. The data used for MI are obtained from both computational and experimental studies, while their integration remains challenging. In our previous study, we reported the integration of these datasets by applying a machine learning model that captures trends hidden in the experimental datasets to compositional data stored in the computational database. In this study, we use the obtained data to construct materials maps, which visualize the relation in the structural features of materials, aiming to support study by the experimental researchers. The map is constructed using a MatDeepLearn (MDL) framework, which implements the graph-based representation of material structures, deep learning, and dimensional reduction for map construction. We evaluate the obtained materials maps through statistical analysis and found that MDL using message passing neural network (MPNN) architecture enables efficient extraction of features that reflect the structural complexity of materials. Moreover, we found that this advantage does not necessarily translate into improved accuracy in the prediction of material properties. We assume this unexpected outcome to the high learning performance inherent in MPNN, which can contribute to the structuring of data points within the materials map.

Modern generative models based on deep learning have made it possible to design millions of hypothetical materials. To screen these candidate materials and identify promising new materials, we need fast and accurate models to predict material properties. Graphical neural networks (GNNs) have become a current research focus due to their ability to directly act on the graphical representation of molecules and materials, enabling comprehensive capture of important information and showing excellent performance in predicting material properties. Nevertheless, GNNs still face several key problems in practical applications: First, although existing nested graph network strategies increase critical structural information such as bond angles, they significantly increase the number of trainable parameters in the model, resulting in a increase in training costs; Second, extending GNN models to broader domains such as molecules, crystalline materials, and catalysis, as well as adapting to small data sets, remains a challenge. Finally, the scalability of GNN models is limited by the over-smoothing problem.

On-device machine learning (ODML) enables powerful edge applications, but power consumption remains a key challenge for resource-constrained devices. To address this, developers often face a trade-off between model accuracy and power consumption, employing either computationally intensive models on high-power cores or pared-down models on low-power cores. Both approaches typically lead to a compromise in user experience (UX). This work focuses on the use of Gated Compression (GC) layer to enhance ODML model performance while conserving power and maximizing cost-efficiency, especially for always-on use cases. GC layers dynamically regulate data flow by selectively gating activations of neurons within the neural network and effectively filtering out non-essential inputs, which reduces power needs without compromising accuracy, and enables more efficient execution on heterogeneous compute cores. These improvements enhance UX through prolonged battery life, improved device responsiveness, and greater user comfort. In this work, we have integrated GC layers into vision and speech domain models including the transformer-based ViT model. Our experiments demonstrate theoretical power efficiency gains ranging from 158x to 30,000x for always-on scenarios. This substantial improvement empowers ODML applications with enhanced UX benefits.

On-device machine learning (ODML) enables intelligent applications on resource-constrained devices. However, power consumption poses a major challenge, forcing a trade-off between model accuracy and power efficiency that often limits model complexity. The previously established Gated Compression (GC) layers offer a solution, enabling power efficiency without sacrificing model performance by selectively gating samples that lack signals of interest. However, their reliance on ground truth labels limits GC layers to supervised tasks. This work introduces the Dynamic Switch Layer (DSL), extending the benefits of GC layers to unsupervised learning scenarios, and maintaining power efficiency without the need for labeled data. The DSL builds upon the GC architecture, leveraging a dynamic pathway selection, and adapting model complexity in response to the innate structure of the data. We integrate the DSL into the SoundStream architecture and demonstrate that by routing up to 80% of samples through a lightweight pass we achieve a 12.3x reduction in the amount of computation performed and a 20.9x reduction in model size. This reduces the on-device inference latency by up to 26.5% and improves power efficiency by up to 21.4% without impacting model performance.

Mobile and embedded machine learning developers frequently have to compromise between two inferior on-device deployment strategies: sacrifice accuracy and aggressively shrink their models to run on dedicated low-power cores; or sacrifice battery by running larger models on more powerful compute cores such as neural processing units or the main application processor. In this paper, we propose a novel Gated Compression layer that can be applied to transform existing neural network architectures into Gated Neural Networks. Gated Neural Networks have multiple properties that excel for on-device use cases that help significantly reduce power, boost accuracy, and take advantage of heterogeneous compute cores. We provide results across five public image and audio datasets that demonstrate the proposed Gated Compression layer effectively stops up to 96% of negative samples, compresses 97% of positive samples, while maintaining or improving model accuracy.

Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unless its size grows significantly larger. For example, it is known that deep networks are exponentially efficient with respect to shallow networks, in the sense that a shallow network must grow exponentially large in order to approximate the functions represented by a deep network of polynomial size. In this work, we extend the study of expressive efficiency to the attribute of network connectivity and in particular to the effect of "overlaps" in the convolutional process, i.e., when the stride of the convolution is smaller than its filter size (receptive field). To theoretically analyze this aspect of network's design, we focus on a well-established surrogate for ConvNets called Convolutional Arithmetic Circuits (ConvACs), and then demonstrate empirically that our results hold for standard ConvNets as well. Specifically, our analysis shows that having overlapping local receptive fields, and more broadly denser connectivity, results in an exponential increase in the expressive capacity of neural networks. Moreover, while denser connectivity can increase the expressive capacity, we show that the most common types of modern architectures already exhibit exponential increase in expressivity, without relying on fully-connected layers.