arXiv subjects

Ashiqur Rasul

Publications and source records attributed to Ashiqur Rasul.

4 recordsLinked to original sources

arXiv2026

A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents

The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of the world's economies. The AI market size is projected to grow from {\$}189 billion in 2023 to {\$}4.8 trillion by 2033. Currently, AI is dominated by large language models (LLMs) that exhibit linguistic and visual intelligence. However, training these models requires a massive amount of data scraped from the web as well as large amounts of energy (50-60 GWh to train GPT-4). Despite these costs, these models often hallucinate, a characteristic that prevents them from being deployed in critical application domains. In contrast, the human brain consumes only 20W of power. What is needed is the next level of AI evolution in which lightweight domain-specific multimodal models, especially compact models with 10--20B parameters for bounded domains, with higher levels of intelligence can reason, plan, and make decisions in dynamic environments with real-time data and prior knowledge, while learning continuously and evolving in ways that enhance future decision-making capability. This will define the next wave of AI, progressing from today's large models, trained with vast amounts of data, to nimble energy-efficient domain-specific agents that can reason and think in a world full of uncertainty. To support such agents, hardware will need to be reimagined to allow system-level energy efficiencies $\geq {1000X}$ over the state of the art for targeted domain tasks, subject to accuracy, latency, and coverage constraints. Such a vision of future AI systems is developed in this work.

cs.AI↗

arXiv2026

DANCE: Dynamic 3D CNN Pruning: Joint Frame, Channel, and Feature Adaptation for Energy Efficiency on the Edge

Modern convolutional neural networks (CNNs) are workhorses for video and image processing, but fail to adapt to the computational complexity of input samples in a dynamic manner to minimize energy consumption. In this research, we propose DANCE, a fine-grained, input-aware, dynamic pruning framework for 3D CNNs to maximize power efficiency with negligible to zero impact on performance. In the proposed two-step approach, the first step is called activation variability amplification (AVA), and the 3D CNN model is retrained to increase the variance of the magnitude of neuron activations across the network in this step, facilitating pruning decisions across diverse CNN input scenarios. In the second step, called adaptive activation pruning (AAP), a lightweight activation controller network is trained to dynamically prune frames, channels, and features of 3D convolutional layers of the network (different for each layer), based on statistics of the outputs of the first layer of the network. Our method achieves substantial savings in multiply-accumulate (MAC) operations and memory accesses by introducing sparsity within convolutional layers. Hardware validation on the NVIDIA Jetson Nano GPU and the Qualcomm Snapdragon 8 Gen 1 platform demonstrates respective speedups of 1.37X and 2.22X, achieving up to 1.47X higher energy efficiency compared to the state of the art.

cs.CV↗

arXiv2024

Tunable Topological Phase Transitions in a Piezoelectric Janus Monolayer

Quantum Spin Hall (QSH) insulators represent a quintessential example of a topological phase of matter, characterized by a conducting edge mode within a bulk energy gap. The pursuit of a tunable QSH state stands as a pivotal objective in the development of QSH-based topological devices. In this study, we employ first-principles calculations to identify three strain-tunable QSH insulators based on monolayer MAlGaTe4 (where M represents Mg, Ca, or Sr). These monolayers exhibit dynamic stability, with no imaginary modes detected in their phonon dispersion. Additionally, they possess piezoelectric properties, rendering them amenable to strain-induced tuning. While MgAlGaTe4 is a normal insulator under zero strain, it transitions into the QSH phase when subjected to external strain. Conversely, CaAlGaTe4 and SrAlGaTe4 already exhibit the QSH phase at zero strain. Intriguingly, upon the application of biaxial strain, these two compounds undergo phase transitions, encompassing metallic (M), normal/trivial insulator (NI), and topological insulator (TI) phases, thereby illustrating their strain-tunable electronic and topological properties. (Ca, Sr)AlGaTe4, in particular, undergo M-TI/TI-M transitions under applied strain, while MgAlGaTe4 additionally experiences an M-NI/NI-M transition, signifying it as a material featuring a metal-insulator transition (MIT). Remarkably, the observation of metal-trivial insulator-topological insulator transitions in MgAlGaTe4 introduces it as a unique material platform in which both MIT and topological phase transitions can be controlled through the same physical parameter. Our study thus introduces a novel material platform distinguished by highly strain-tunable electronic and topological properties, offering promising prospects for the development of next-generation, low-power topological devices.

cond-mat.mtrl-sci↗

arXiv2023

Topological, or Non-topological? A Deep Learning Based Prediction

Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology and graph neural network which offers an accuracy of 91.4% and an F1 score of 88.5% in classifying topological vs. non-topological materials, outperforming the other state-of-the-art classifier models. The incorporation of the graph neural network encodes the underlying relation between the atoms into the model based on their own crystalline structures and thus proved to be an effective method to represent and process non-euclidean data like molecules with a relatively shallow network. The persistent homology pipeline in the suggested neural network is capable of integrating the atom-specific topological information into the deep learning model, increasing robustness, and gain in performance. It is believed that the presented work will be an efficacious tool for predicting the topological class and therefore enable the high-throughput search for novel materials in this field.

cond-mat.mtrl-sci↗