Editors:
Tianyin Xu, Akshitha Sriraman, Baris Kasikci, and Dong Du
Wafer-Scale AI Compute: A System Software Perspective
This article originally appeared in USENIX ;login: magazine, shepherded by Rik Farrow. As AI models grow larger and more complex, traditional computing architectures are hitting performance and efficiency limits. A new class of hardware, wafer-scale AI chips, pushes these boundaries by integrating hundreds of thousands of cores and massive on-chip memory onto a single wafer.
Barbarians at The Gate: How AI is Upending Systems Research
Editor’s note: For the first article of the The Next Horizon of System Intelligence series, we invite the ADRS team from Berkeley to share their recent work which has raised very active discussions in the community. AI is no longer just tuning systems as a “black box.” It’s now rewriting their core algorithms by treating
The Next Horizon of System Intelligence
Editor’s note: The authors are opening a blog series on the timely topic of system intelligence and the future of systems research with the intelligence as a new capability. They are actively looking for contributors to share ideas, viewpoints, and experiences. Why This Blog Series? Generative AI, as represented by Large Language Models (LLMs), has
Ethical and Moral Fraying due to Intellectual Conflicts in Paper Reviews
Editor’s note: This article is cross-posted from SIGARCH. We believe that the article is of importance to SIGOPS conferences and thus republish as a SIGOPS blog post to disseminate the message further. In computer architecture conferences, “expert”/”knowledgeable” reviewers often are conflicted intellectually with at least some of the submissions they review (e.g., the submissions directly
Lessons from Five Years of Artifact Evaluation at EuroSys
Reproducibility is a cornerstone of scientific progress, yet in systems research, it remains a persistent challenge. Over the past five years, the Artifact Evaluation (AE) process at the European Conference on Computer Systems (EuroSys) has aimed to address this challenge head-on. In this blog post, we reflect on what we have learned from organizing AE
The role of LLMs in academic reviewing
The past few years have witnessed a staggering acceleration in the capabilities of large language models (LLMs). What began as an intriguing toy for autocomplete has evolved into sophisticated tools capable of summarizing research papers, drafting technical arguments, and even simulating expert‑level discussion. As these models continue to improve at an astonishing rate, the question