In-network machine learning inference provides high throughput and low latency. It is ideally located within the network, power efficient, and improves applications' performance. Despite its advantages, the bar to in-network machine learning research is ...

Achieving high performance in machine learning workloads is a crucial yet difficult task. To achieve high runtime performance on hardware platforms such as GPUs, graph-based executions such as CUDA graphs are often used to eliminate CPU runtime ...

Tensor kernels in machine learning (ML) often correspond to pure mathematical expressions, making term rewriting an attractive strategy for optimization and mapping to specialized hardware accelerators. However, existing ML intermediate representations (...