chetanyagoyal

GSoC 2024 Progress Report

Relational Framework for Prompt-based Automatic Layout Generation with Glayout

• Name :- Chetanya Goyal
• Slack :- Chetanya Goyal
• Email :- chinnidude@gmail.com
• GitHub :- chetanyagoyal

Project Overview

This project aims to develop a system to generate DRC and LVS-clean analog layouts from natural language prompts, which will streamline layout creation and enable high-level circuit specification. This involves the use of the pre-existing GLayout API in conjunction with a specially created syntax (termed as strict-syntax) and a Large Language Model.

xvdp

lucidrains

# link to package https://github.com/lucidrains/slot-attention

import torch
from torch import nn

class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
    def forward(self, x):

lucidrains

import torch
from torch import nn

def expand_dim(t, dim, k):
    t = t.unsqueeze(dim)
    expand_shape = [-1] * len(t.shape)
    expand_shape[dim] = k
    return t.expand(*expand_shape)

class PKM(nn.Module):

orionpax00

Attention

The Attention mechanism in Deep Learning is based off the concept of directing model's focus, and it pays greater attention to certain factors when processing the data.[1]

---

SeungjunNah

# reference: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive

import os
import requests
import argparse

def download_file_from_google_drive(id, destination):
    def get_confirm_token(response):
        for key, value in response.cookies.items():
            if key.startswith('download_warning'):

iandanforth

A Checklist for Reproducibility in Reinforcement Learning

From a slide in a NeurIPS 2018 keynote by Joelle Pineau

For all algorithms presented, check if you include:

• A clear description of the algorithm.
• An analysis of the complexity (time, space, sample size) of the algorithm.
• A link to downloadable source code, including all dependencies.

bushaev

for t in range(num_interations):
    dw[t] = compute_gradient(x, y)
    
    if dw[t] * dw[t - 1] > 0:
        step_size = min(step_size * incFactor, step_size_max)
    elif dw[t] * dw[t - 1] < 0:
        step_size = max(step_size * decFactor, step_size_min)
    
    w[t] = w[t - 1] - sign(dw[t]) * step_size

MacTuitui

//roughly like this

function draw(){
  var frac =  (gCount%200)*1.0/(100)
  if(frac>1 ) frac = 2-frac
  var frac2 =  (gCount%200)*1.0/(200)
  gFrac = 1-frac2
  val s = 40

  for(k <- 0 until s){

nulledge