Very happy to announce that Giacomo Verardo defended his doctoral thesis! @marchiesa was the co-advisor and @GQMaguireJr provided stellar insights. More details and the link to Giacomo's thesis:
https://t.co/SH9OnyRDiU
New postdoc opening in my DAI project on creating a highly energy-efficient and cheap platform for AI inferencing! The project blog gives more information on our recent work and the overall project vision:
https://t.co/wRXSPBWZZT
https://t.co/b1uzX2DGz7
New ad for a PhD position, new aurora picture! Large Language Models keep getting bigger and more capable, we keep trying to make inferencing much more efficient:
https://t.co/GN5rSppcMt
More details about work, including links to the paper and source code at the blog site below:
https://t.co/1UUjOD1pCb
Xiangyu Shi @GQMaguireJr@marchiesa (5/5)
Which form of communication should Large Language Models (LLMs) use when collaborating on a problem, e.g., one or two models processing the context and another model answering a question without seeing the context itself: plain text, hidden state, or some other way? (1/5)
Interestingly enough, on two data sets KVComm even exceeds the performance of an approach that gives both the context and the question in text to the model answering the question. Our approach works even for different LLMs that are fine-tuned from the same base model. (4/5)
Happy to announce that Daniel Perez successfully defended his PhD thesis on February 12, 2026! This thesis uses ML to solve resource allocation optimization problems and demonstrates 100x scaling in problem size. https://t.co/qNLasEBR1N
Congratulations @WangChangjie13 on receiving the 2025 Applied Networking research prize for our NetConfEval work! 🏆
Now, let’s work on making LLMs also reliable and sustainable for code generation! 🚀
https://t.co/rh55z4F67V
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At CoNEXT ’24, @Skazza_94 presented our FAJITA paper for running stateful network functions at very high rates (~1.4 Tbps). PDF + video are at the @ERC_Research CoG ULTRA blog post below. Click below to see something perhaps unexpected in this work.
https://t.co/P0B47cj4tu
2/3
Unless the number of so-called “elephant flows” is very small, spreading incoming packets among the cores using plain Receive Side Scaling (RSS) outperforms existing approaches that perform fine-grained flow accounting and load-balancing.
I am continuing to look for two more PhD students who wish to work with us on creating a dramatically more energy efficient and cheaper platform for AI inferencing (deadline Jan 17, 2025):
https://t.co/Gb9KXk8xhY
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@marchiesa We cannot promise sunsets and Aurora sightings like these, but we can promise an exciting research environment and lots of academic freedom!
3/3