Scaling Laws provide a valuable lens in guiding model design and computational budgets. Our recent work extends this lens to the realm of _fine-grained_ sparsity. Check out our #ICLR2025 paper, and the thread below from lead-author @tjin.bsky.social summarizing our findings.

At Google, we announced the latest generation of our AI supercomputers (TPUs) -- Ironwood -- this week. Check out the blogpost in quote for the highlights. blog.google/products/googl… Pointers to deep-dives and more technical details in thread. [contd...👇]

Together with Lisa Hsu (Meta), we have been hosting the Computer Architecture Podcast -- we recently crossed 50K downloads. Check out our latest episode with Prof. Arka Basu: comparchpodcast.podbean.com -- we discuss GPUs, but a different vantage point than AI which is all the rage.

Starting with this exciting line of work from @tjin.bsky.social and colleagues at MIT. We tackle the question of: Can we train LLMs to parallelize autoregressive decoding automatically, backed by a performant runtime to exploit this parallelism for improved inference speedup?

Hello world! Dipping my toes into social media. My excellent intern(s) at Google with whom I have had the pleasure of working, were kind enough to nudge me to help signal-boost their work. Will also try to share updates on TPUs, AI chips & systems, and computer architecture.