Early routability prediction helps designers and tools perform preventive measures so that design rule violations can be avoided in a proactive manner. However, it is a huge challenge to have a predictor that is both accurate and fast. In this work, we study how to leverage convolutional neural network to address this challenge. The proposed method, called RouteNet, can either evaluate the overall routability of cell placement solutions without global routing or predict the locations of DRC (Design Rule Checking) hotspots. In both cases, large macros in mixed-size designs are taken into consideration. Experiments on benchmark circuits show that RouteNet can forecast overall routability with accuracy similar to that of global router while using substantially less runtime. For DRC hotspot prediction, RouteNet improves accuracy by 50% compared to global routing. It also significantly outperforms other machine learning approaches such as support vector machine and logistic regression.

https://dl.acm.org/doi/pdf/10.1145/3240765.3240843

RouteNet: routability prediction for mixed-size designs using convolutional neural network

Design rule check (DRC) violations after detailed routing prevent a design from being taped out. To solve this problem, state-of-the-art commercial EDA tools global-route the design to produce a global-route congestion map; this map is used by the placer to optimize the placement of the design to reduce detailed-route DRC violations. However, in sub-14nm processes and beyond, DRCs arising from multiple patterning and pin-access constraints drastically weaken the correlation between global-route congestion and detailed-route DRC violations. Hence, the placer|based on the global-route congestion map|may leave too many detailed-route DRC violations to be fixed manually by designers. In this paper, we present a method that employs (1) machine-learning techniques to effectively predict detailed-route DRC violations after global routing and (2) detailed placement techniques to effectively reduce detailed-route DRC violations. We demonstrate on several layouts of a sub-14nm industrial design that this method predicts the locations of 74% of the detailed-route DRCs (with false positive prediction rate below 0.2%) and automatically reduces the number of detailed-route DRC violations by up to 5x. Whereas previous works on machine learning for routability [30] [4] have focused on routability prediction at the floorplanning and placement stages, ours is the first paper that not only predicts the actual locations of detailed-route DRC violations but furthermore optimizes the design to significantly reduce such violations.

Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning

Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the design of complex hydraulic systems, pipe systems and logistic networks. Existing solutions typically consist of greedy algorithms and hard-coded heuristics. As such, existing approaches suffer from a lack of model flexibility and non-optimum solutions. As an alternative approach, this work presents a deep reinforcement learning method for solving the global routing problem in a simulated environment. At the heart of the proposed method is deep reinforcement learning that enables an agent to produce an optimal policy for routing based on the variety of problems it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning. Conjoint optimization mechanism is explained and demonstrated in details; the best network structure and the parameters of the learned model are explored. Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. The results indicate that the approach can outperform the benchmark method of a sequential A* method, suggesting a promising potential for deep reinforcement learning for global routing and other routing or path planning problems in general. Another major contribution of this work is the development of a global routing problem sets generator with the ability to generate parameterized global routing problem sets with different size and constraints, enabling evaluation of different routing algorithms and the generation of training datasets for future data-driven routing approaches.

A Deep Reinforcement Learning Approach for Global Routing

In advanced technology nodes, physical design engineers must estimate whether a standard-cell placement is routable (before invoking the router) in order to maintain acceptable design turnaround time. Modern SoC designs consume multiple compute servers, memory, tool licenses and other resources for several days to complete routing. When the design is unroutable, resources are wasted, which increases the design cost. In this work, we develop machine learning-based models that predict whether a placement solution is routable without conducting trial or early global routing. We also use our models to accurately predict iso-performance Pareto frontiers of utilization, aspect ratio and number of layers in the back-end-of-line (BEOL) stack. Furthermore, using data mining and machine learning techniques, we develop new methodologies to generate training examples given very few placements. We conduct validation experiments in three foundry technologies (28nm FDSOI, 28nm LP and 45nm GS), and demonstrate accuracy ≥ 85.9% in predicting routability of a placement. Our predictions of Pareto frontiers in the three technologies are pessimistic by at most 2% with respect to the maximum achievable utilization for a given design in a given BEOL stack.

BEOL stack-aware routability prediction from placement using data mining techniques

Detecting and preventing routing violations has become a critical issue in physical design, especially in the early stages. Lack of correlation between global and detailed routing congestion estimations and the long runtime required to frequently consult a global router adds to the problem. In this paper, we propose a machine learning framework to predict detailed routing short violations from a placed netlist. Factors contributing to routing violations are determined and a supervised neural network model is implemented to detect these violations. Experimental results show that the proposed method is able to predict on average 90% of the shorts with only 7% false alarms and considerably reduced computational time.

A machine learning framework to identify detailed routing short violations from a placed netlist

Routability is one of the primary objectives in placement. There have been many researches on forecasting routing problems and improving routability in placement but no perfect solution is found. Most traditional routability-driven placers aim to improve global routing result, but true routability lies in detailed routing. Predicting detailed routing routability in placement is extremely difficult due to the complexity and uncertainty of routing. In this paper, we propose a new detailed routing routability prediction model based on supervised learning. After extracting key features in placement and detailed routing, multivariate adaptive regression is performed to train the connection between these two stages. Using a well-trained model, most design rule violations after detailed routing can be foreseen in placement stage. Experiments show that our average prediction accuracy is 79.8%, which is comparable with other state-of-art routability estimation techniques.

An accurate detailed routing routability prediction model in placement

As VLSI technology continuously scales down, robust clock tree synthesis (CTS) has become increasingly critical in an attempt to generate a high-performance synchronous chip design. Clock skew resulted by process variations can be significantly different from the nominal value. In this paper, we propose an efficient buffer sizing algorithm to solve the skew optimization problem in presence of process variations. By analyzing the influence of process variations on wire delay and buffer delay, we make a quantitative estimation of the skew distribution under Monte-Carlo SPICE simulations. The number and size of buffers on some critical paths are rearranged to reduce the skew results under process variations. Experiment results which are evaluated on ISPD 2010 benchmarks show that our algorithm achieves a significant 58% reduction on worst skew with only 6% increase on power consumption.

An efficient buffer sizing algorithm for clock trees considering process variations

Physical Unclonable Function (PUF) is one of the most efficient technique to generate unique and random identification for chip authentication. Ring oscillator (RO) PUF takes advantage of delay variations of a pair of ROs, which is easy to implement on FPGAs. An important consideration for FPGA based RO PUF is how to eliminate systematic variation without reducing the number of output bits. To address this problem, we introduce high performance RO organization and comparison framework. Moreover, an enhanced configurable RO, which has up to 512 different configurations but only occupies one FPGA slice, is proposed to improve the reliability and output bits number. Experimental results demonstrate that our PUF achieves best value on bit-aliasing rate (50.37%) compared with other existing configurable RO PUFs. The output bits number also increases by the factors of 2.1–9.2.

An efficient framework for configurable RO PUF

As technology advances, modern physical synthesis flows pose layer directive and scenic constraints to global routing. Respecting these constraints in global routing is critical to achieve timing closure. In this paper, we propose a 3-D global routing algorithm which simultaneously respects layer directive constraints and scenic constraints. The routing algorithm is based on iterative ripping-up and rerouting. The core components of the algorithm include a 3-D A∗ search which strictly obeys given constraints, and a net ordering approach which helps to achieve high-quality routing results. Experimental results demonstrate the effectiveness of the routing algorithm.

Zhuwei Chen

Papers

An accurate detailed routing routability prediction model in placement

An efficient buffer sizing algorithm for clock trees considering process variations

An efficient framework for configurable RO PUF

3-D global routing with layer directive and scenic constraints