The importance of open-source hardware and software has been increasing. However,
despite GPUs being one of the more popular accelerators across various applications, there …

Machine learning (ML) inference workloads present significantly different challenges than
ML training workloads. Typically, inference workloads are shorter running and under-utilize …

The breakdown in Moore's Law and Dennard Scaling is leading to drastic changes in the
makeup and constitution of computing systems. For example, a single chip integrates 10 …

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 …

Large Language Models increasingly rely on distributed techniques for their training and
inference. These techniques require communication across devices which can reduce …

Quantum computing holds great promise for accelerating computational tasks, but they are
still not accessible. To fill this gap, quantum computing simulators have been widely used for …

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 this work we have enhanced gem5's GPU model support to add Matrix Core Engines
(MCEs). Specifically, on the AMD MI200 and MI300 GPUs that gem5 supports, these MCEs …

In recent years, we have been enhancing and updating gem5's GPU support [1]. First, we
have enhanced gem5's GPU support for ML workloads such that gem5 can now run [2] …