In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In this paper, we propose an extension to this prior work (Mirhoseini et al., 2020). We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.
For its advantage in GPU acceleration and less dependency on human experts, machine learning has been an emerging tool for solving the placement and routing problems, as two critical steps in modern chip design flow. Being still in its early stage, there are fundamental issues: scalability, reward design, and end-to-end learning paradigm etc. To achieve end-to-end placement learning, we first propose a joint learning method termed by DeepPlace for the placement of macros and standard cells, by the integration of reinforcement learning with a gradient based optimization scheme. To further bridg…
Chip placement has been one of the most time consuming task in any semi conductor area, Due to this negligence, many projects are pushed and chips availability in real markets get delayed. An engineer placing macros on a chip also needs to place it optimally to reduce the three important factors like power, performance and time. Looking at these prior problems we wanted to introduce a new method using Reinforcement Learning where we train the model to place the nodes of a chip netlist onto a chip canvas. We want to build a neural architecture that will accurately reward the agent across a wide…
Department of EECS, UC Berkeley, CA, USA（UC伯克利电子工程与计算机科学系）；SambaNova Systems Inc., Palo Alto, CA, USA（SambaNova Systems公司）；Computer Science Department, Stanford University, CA, USA（斯坦福大学计算机科学系）
Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pre…
Physics-Guided Geometric Diffusion for Macro Placement Generation
POSTECH Institute of Artificial Intelligence（POSTECH人工智能研究所）；KAIST InnoCORE LLM（韩国科学技术院InnoCORE语言模型实验室）；Seoul National University（首尔国立大学）；Pohang University of Science and Technology（釜山科学技术大学）
University of Amsterdam（阿姆斯特丹大学）；Qualcomm AI Research（高通人工智能研究院）；QT Technologies Ireland Limited（QT技术爱尔兰有限公司）；QUALCOMM India Private Limited（高通印度私人有限公司）
Macro placement is the problem of placing memory blocks on a chip canvas. It can be formulated as a combinatorial optimization problem over sequence pairs, a representation which describes the relative positions of macros. Solving this problem is particularly challenging since the objective function is expensive to evaluate. In this paper, we develop a novel approach to macro placement using Bayesian optimization (BO) over sequence pairs. BO is a machine learning technique that uses a probabilistic surrogate model and an acquisition function that balances exploration and exploitation to effici…
