This talk will explore how RISC-V architecture can accelerate custom workloads, focusing on AI/ML applications. We’ll start by examining the RISC-V ecosystem and its increasing relevance in the software development landscape. By looking at OpenBLAS, a highly optimized linear algebra library, we'll demonstrate how it's been optimized for RISC-V hardware, leading to significant performance gains in AI/ML tasks.

We’ll also highlight the ongoing work at RISC-V International on the matrix multiply extension, which promises to further enhance performance for these critical operations. Through practical examples, the talk will highlight why RISC-V is a strong candidate for optimizing workloads requiring high computational efficiency.

This session will offer valuable insights into how RISC-V can meet demanding performance needs while providing flexibility for custom workload optimization.