Transfer learning in natural language processing (NLP) uses increasingly large models that
tackle challenging problems. Consequently, these applications are driving the requirements …

As large language models (LLMs) become widespread in various application domains, a
critical challenge the AI community is facing is how to train these large AI models in a cost …

Simulating all threads in a scaled GPU workload results in prohibitive simulation cost. Cycle-
level simulation is orders of magnitude slower than native silicon, the only solution is to …

Transfer learning in natural language processing (NLP), as realized using models like BERT
(Bi-directional Encoder Representation from Transformer), has significantly improved …

To exploit the ever increasing compute capabilities offered by GPU hardware, GPU-compute
workloads have evolved from simple computational kernels to large-scale programs with …

Scaling neural network models has delivered dramatic quality gains across ML problems.
However, this scaling also increased the reliance on efficient distributed training techniques …

Modern accelerators like GPUs are increasingly executing independent operations
concurrently to improve the device's compute utilization. However, effectively harnessing it …

In recent years deep neural networks (DNNs) have emerged as an important application
domain driving the requirements for future systems. As DNNs get more sophisticated, their …

In recent years deep neural networks (DNNs) have emerged as an important application
domain driving the requirements for future systems. As DNNs get more sophisticated, their …

With the share of machine learning (ML) workloads in data centers rapidly increasing, cloud
providers are beginning to incorporate accelerators such as tensor processing units (TPUs) …