When training a deep learning (DL) model, input data are pre-processed on CPUs and
transformed into tensors, which are then fed into GPUs for gradient computations of model …

With the rapid development of deep learning, training Big Models (BMs) for multiple
downstream tasks becomes a popular paradigm. Researchers have achieved various …

Input data preprocessing is a common bottleneck in machine learning (ML) jobs, that can
significantly increase training time and cost as expensive GPUs or TPUs idle waiting for …

The Deep Learning (DL) training process consists of multiple phases—data augmentation,
training, and validation of the trained model. Traditionally, these phases are executed either …

The deep learning (DL) training process consists of multiple phases—data augmentation,
training, and validation of the trained model. Traditionally, these phases are executed either …

Edge computing has been emerging as a popular scenario for model inference. However,
the inference performance on edge devices (eg, Multi-Core DSP, FGPA, etc.) suffers from …

With the rapid up-scaling of transformer-based large language models (LLM), training these
models is becoming increasingly demanding on novel parallel training techniques. Tensor …

Reliable and fast channel estimation is crucial for next-generation wireless networks
supporting a wide range of vehicular and low-latency services. Recently, deep learning (DL) …

The advancements in AI/ML accelerators have made the core AI/ML computation relatively
insignificant in application pipelines. For example, inferencing only accounts for 3% of the …

With the increasing number of deep learning applications, the optimization of deep learning
model performance has become a central focus of research. One of the critical indicators is …