Speaker:
Simran Arora
Talk Title:
Pareto-efficient AI systems: Expanding the quality and efficiency frontier of AI
Date and Location:
Wednesday, April 16, 2025
Bahen Centre for Information Technology, BA 3200
This lecture is open to the public. No registration is required, but space is limited.
Abstract:
In this talk, we piece-by-piece build up to BASED, a simple language model architecture that expands the Pareto frontier between quality and throughput efficiency. The Transformer, AI’s workhorse architecture, is memory hungry, limiting its throughput. This has led to a Cambrian explosion of alternate architecture candidates proposed across prior work. Prior work paints an exciting picture: there are architectures that are asymptotically faster than the Transformer, while also matching its quality. However, I ask, if we’re using asymptotically faster building blocks, what if anything are we giving up in quality? 1. In part one, we understand the tradeoff space and show there’s no free lunch. I present my work to identify and explain the fundamental quality and efficiency tradeoffs between different classes of language model architectures. 2. In part two, we measure how existing architecture candidates fare along on the tradeoff space. While many proposed architectures are asymptotically fast, they struggle to achieve wall-clock speed compared to Transformers. I present my work on ThunderKittens, a GPU programming library to help address the bottleneck of mapping AI algorithms to AI hardware. 3. In part three, we expand the Pareto frontier of the tradeoff space. I present the BASED architecture, which is built from familiar and hardware-efficient components. In culmination, I released a suite of state-of-the-art 8B-405B parameter Transformer-free language models, per standard evaluations, all on an academic budget. Given the massive investment into AI models, this work blending AI and systems has had significant impact and adoption in research, open-source, and industry.
About Simran Arora:
Simran Arora is a PhD student at Stanford University advised by Chris Ré. Her research blends AI and systems towards expanding the Pareto frontier between AI capabilities and efficiency. Her machine learning research has appeared as Oral and Spotlight presentations at NeurIPS, ICML, and ICLR, including an Outstanding Paper award at NeurIPS and Best Paper award at ICML ES-FoMo. Her systems work has appeared at VLDB, SIGMOD, CIDR, and CHI, and her systems artifacts are widely used in research, open-source, and industry. In 2023, Simran created and taught the CS229s Systems for Machine Learning course at Stanford. She has also been supported by a SGF Sequoia Fellowship and the Stanford Computer Science Graduate Fellowship.