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The significance of distributed learning and inference algorithms in Internet of Things (IoT) network is growing since they flexibly distribute computation load between IoT devices and the infrastructure, enhance data privacy, and minimize…
As the foundational component of versatile AI applications, training an multimodal large language model (MLLM) relies on multimodal datasets with dynamic modality mixture proportions and sample length distributions. However, existing MLLM…
Visible light communication (VLC) backscatter has been proposed as a wireless access option for Internet of Things (IoT). However, the throughput of the state-of-the-art VLC backscatter is limited by simple single-carrier pulsed modulation…
The applications require coverage of the whole monitored area for long periods of time. Clustering is a way to reduce communications, minimize energy consumption and organize messages among the cluster head and their members. The message…
Chiplets have become a common methodology in modern chip design. Chiplets improve yield and enable heterogeneity at the level of cores, memory subsystem and the interconnect. Convolutional Neural Networks (CNNs) have high computational,…
IoT devices are sorely underutilized in the medical field, especially within machine learning for medicine, yet they offer unrivaled benefits. IoT devices are low-cost, energy-efficient, small and intelligent devices. In this paper, we…
Timing synchronization (TS) is vital for orthogonal frequency division multiplexing (OFDM) systems, which makes the discrete Fourier transform (DFT) window start at the inter-symbol-interference (ISI)-free region. However, the multi-path…
The proliferation of data-intensive applications, ranging from large language models to key-value stores, increasingly stresses memory systems with mixed read-write access patterns. Traditional half-duplex architectures such as DDR5 are…
In clustering tasks, it is essential to structure the feature space into clear, well-separated distributions. However, because short text representations have limited expressiveness, conventional methods struggle to identify cluster centers…
Optics offers unique opportunities for reducing energy in information processing and communications while resolving the problem of interconnect bandwidth density inside machines. Such energy dissipation overall is now at environmentally…
Random access schemes are widely used in IoT wireless access networks to accommodate simplicity and power consumption constraints. As a result, the interference arising from overlapping IoT transmissions is a significant issue in such…
Chiplet architecture is an emerging architecture for quantum computing that could significantly increase qubit resources with its great scalability and modularity. However, as the computing scale increases, communication between qubits…
Large language models (LLMs) achieve strong capabilities by scaling model capacity and training data, yet many real-world deployments rely on smaller models trained or adapted from low-resource data. This gap motivates the need for…
All-gather collective communication is one of the most important communication primitives in parallel and distributed computation, which plays an essential role in many HPC applications such as distributed Deep Learning (DL) with model and…
In training of modern large natural language processing (NLP) models, it has become a common practice to split models using 3D parallelism to multiple GPUs. Such technique, however, suffers from a high overhead of inter-node communication.…
Interconnecting devices in an indoor environment using the illumination system and white light emitting diodes (LED) requires adaptive networking techniques that can provide network access for multiple users. Two techniques based on…
The fundamental success of large language models hinges upon the efficacious implementation of large-scale distributed training techniques. Nevertheless, building a vast, high-performance cluster featuring high-speed communication…
Multimodal deep learning models enable joint learning across heterogeneous data sources, including text, images, and video, but their rapid scaling introduces significant memory and communication bottlenecks. As model sizes and sequence…
Enterprise LLM deployment faces a critical scalability challenge: organizations must optimize models systematically to scale AI initiatives within constrained compute budgets, yet the specialized expertise required for manual optimization…
In this paper, we investigate the downlink communication challenges in heavy-load Internet-of-Things (IoT) networks supported by frequency-division-duplexing (FDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO)…