[CMU AI Seminar] Special! Oct 4 at 12pm (GHC 8102 & Zoom) -- Felix Petersen (Stanford) -- Differentiable Logic Gate Networks and Sorting Networks -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you *this Friday (10/4)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for a special installment of this semester's
*CMU AI Seminar*, sponsored by SambaNova Systems <https://sambanova.ai/>.
The seminar will be held in GHC 8102 *with pizza provided *and will be
streamed on Zoom.

*📨 Please reply to this email for a signup sheet if you would like to meet
with Felix this week.*

To learn more about the seminar series or to see the future schedule,
please visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.

On *this Friday (10/4)*, *Felix Petersen* (Stanford) will be giving a talk
titled *"Differentiable Logic Gate Networks and Sorting Networks**" *to
explain how such networks enable *optimizing circuits for extremely
efficient ML inference* and improving weakly- and self-supervised losses.

*Title*: Differentiable Logic Gate Networks and Sorting Networks

*Talk Abstract*: The ability to differentiate conventionally
non-differentiable algorithms and operations is crucial for many advanced
machine learning tasks. After an introduction of the topic, we will dive
into differentiable sorting networks for learning-to-rank, top-k
classification learning, and for self-supervised learning. In the second
half, we will cover differentiable logic gate networks, which enable
directly optimizing logical circuits along the paradigm of "the hardware is
the model". Compared to the SOTA in the most efficient hardened models, we
achieve chip area reductions ranging from 16x to over 200x, and latency
reductions ranging from 130x to 31,000x. Compared to Int8 inference on
GPUs, this corresponds to energy savings of around 7 orders of magnitude.

*Speaker Bio:* Felix Petersen <http://petersen.ai> is a postdoctoral
researcher at Stanford University in Stefano Ermon's group; he primarily
researches differentiable relaxations in machine learning, with
applications to extremely efficient inference and weakly-supervised
learning. He runs the Differentiable Almost Everything workshop, and has
previously worked at the University of Konstanz, TAU, DESY, PSI, and CERN.

*In person: *GHC 8102
*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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