Intern Challenge: Placement Problem - Fork this repo when you start!

Welcome to the par.tcl 2026 ML Sys intern challenge! Your task is to solve a placement problem involving standard cells (small blocks) and macros (large blocks). The primary goal is to minimize overlap between blocks. Wirelength is also evaluated, but overlap is the dominant objective. A valid placement must eventually ensure no blocks overlap, but we will judge solutions by how effectively you reduce overlap and, secondarily, how well you handle wirelength.

The deadline is when all intern slots for summer 2026 are filled. We will review submissions on a rolling basis.

Problem Statement

• Objective: Place a set of standard cells and macros on a chip layout to minimize overlap (most important) and wirelength (secondary).
  • Overlap will be measured as num overlapping cells / num total cells, though you are encouraged to define and implement your own overlap loss function if you think it’s better.
  • Solving this problem will require designing a strong overlap loss, tuning hyperparameters, and experimenting with optimizers. Creativity is encouraged — nothing is off the table.
• Input: Randomly generated netlists.
• Output: Average normalized overlap (primary metric) and wirelength (secondary metric) across a set of randomized placements.

Submission Instructions

1. Fork this repository.
2. Solve the placement problem using your preferred tools or scripts.
3. Run the first 10 tests to evaluate your solution and obtain the overlap and wirelength metrics. Report Average Overlap, Wirelength and total Runtime. Test cases 11 and 12 are extra credit, give them a shot if you have some time.
4. Submit a pull request with your updated leaderboard entry and instructions for me to access your actual submission (it's fine if it's public).

Note: You can use any libraries or frameworks you like, but please ensure that your code is well-documented and easy to follow.

Also, if you think there are any bugs in the provided code, feel free to fix them and mention the changes in your submission.

You may submit multiple solutions to try and increase your score.

We will review submissions on a rolling basis.

Leaderboard (sorted by overlap)

Rank	Name	Overlap	Wirelength (um)	Runtime (s)	Notes
1	Brayden Rudisill	0.0000	0.2611	50.51	Timed on a mac air
2	manuhalapeth	0.0000	0.2630	196.8
3	Neil Teje	0.0000	0.2700	24.00s
4	Leison Gao	0.0000	0.2796	50.14s
5	William Pan	0.0000	0.2848	155.33s
6	Ashmit Dutta	0.0000	0.2870	995.58	Spent my entire morning (12 am - 6 am) doing this :P
7	Pawan Paleja	0.0000	0.3311	1.74s	Implemented hint for loss func, cosine annealing on learning rate with warmup, std annealing on lambda weight. Used optuna to tune hyperparam. Tested on gh codespaces 2-core.
8	Shashank Shriram	0.0000	0.3312	11.32	🏎️💥
9	Gabriel Del Monte	0.0000	0.3427	606.07
10	Aleksey Valouev	0.0000	0.3577	118.98
11	Mohul Shukla	0.0000	0.5048	54.60s
12	Ryan Hulke	0.0000	0.5226	166.24
13	Neel Shah	0.0000	0.5445	45.40	Zero overlaps on all tests, adaptive schedule + early stop
14	Nawel Asgar	0.0000	0.5675	81.49	Adaptive penalty scaling with cubic gradients and design-size optimization
15	Shiva Baghel	0.0000	0.5885	491.00	Stable zero-overlap with balanced optimization
16	Vansh Jain	0.0000	0.9352	86.36
17	Akash Pai	0.0006	0.4933	326.25s
18	Zade Mahayni	0.00665	0.5157	127.4	Will try again tomorrow
19	Nithin Yanna	0.0148	0.5034	247.30s	aggressive overlap penalty with quadratic scaling
20	Sean Ko	0.0271	.5138	31.83s	lr increase, decrease epoch, increase lambda overlap and decreased lambda wire_length + log penalty loss
21	Keya Gohil	0.0155	0.4678	1513.07	Still working
22	Prithvi Seran	0.0499	0.4890	398.58
23	partcl example	0.8	0.4	5	example
24	Add Yours!

To add your results:
Insert a new row in the table above with your name, overlap, wirelength, and any notes. Ensure you sort by overlap.

Good luck!