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MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
https://arxiv.org/abs/2211.13382
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Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement learning has become an emerging autonomous tool. However, the learning-centric method is still in its early stage, impeded by a massive design space of size ten to the order of a few thousand. This work presents MaskPlace to automatically generate a valid chip layout design within a few hours, whose performance can be superior or comparable to recent advanced approaches. It has several appealing benefits that prior arts do not have. Firstly, MaskPlace recasts placement as a problem of learning pixel-level visual representation to comprehensively describe millions of modules on a chip, enabling placement in a high-resolution canvas and a large action space. It outperforms recent methods that represent a chip as a hypergraph. Secondly, it enables training the policy network by an intuitive reward function with dense reward, rather than a complicated reward function with sparse reward from previous methods. Thirdly, extensive experiments on many public benchmarks show that MaskPlace outperforms existing RL approaches in all key performance metrics, including wirelength, congestion, and density. For example, it achieves 60%-90% wirelength reduction and guarantees zero overlaps. We believe MaskPlace can improve AI-assisted chip layout design.
https://laiyao1.github.io/maskplace/
Forwarded from HN Best Comments
Re: I don’t want to be an internet person

> I don’t want to be anything like these people. I don’t want to be an internet person.

I love this. I got a similar vibe from people who were "too good at IRC," way back in the day. They had a constant, sarcastic, tired energy about them. They had difficulty being genuine about anything. They knew so much and yet they were so stuck in their life somehow. And that sucked the life out of them.

It's like they were too tied to this vague idea of being online that they weren't willing to sacrifice it to have a better life.

The Internet is a tool, not an endpoint.

mattgreenrocks, 1 day ago
What Does the “Mean” Really Mean?

Abstract:

The arithmetic average of a collection of observed values of a homogeneous collection of quantities is often taken to be the most representative observation. There are several arguments supporting this choice the moment of inertia being the most familiar. But what does this mean?
In this note, we bring forth the Kolmogorov-Nagumo point of view that the arithmetic average is a special case of a sequence of functions of a special kind, the quadratic and the geometric means being some of the other cases. The median fails to belong to this class of functions. The Kolmogorov-Nagumo interpretation is the most defensible and the most definitive one for the arithmetic average, but its essence boils down to the fact that this average is merely an abstraction which has meaning only within its mathematical set-up.

https://arxiv.org/pdf/2003.01973.pdf

f -> average -> f^(-1)
just like change of bases
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Forwarded from cyberchaos & mathюки
#edu The Forward-Forward Learning Algorithm

The Forward-Forward algorithm replaces the forward and
backward passes of backpropagation by two forward passes, one with positive
(i.e. real) data and the other with negative data which could be generated by the
network itself. Each layer has its own objective function which is simply to have
high goodness for positive data and low goodness for negative data.

the paper itself: https://www.cs.toronto.edu/~hinton/FFA13.pdf
a nice explanatory video: https://youtu.be/rVzDRfO2sgs
fun page

"The webpages collected here list information about classes of mathematical structures. The aim is to have a central place to check what properties are known about these structures."

https://math.chapman.edu/~jipsen/structures/doku.php