TAG: Learning Circuit Spatial Embedding From Layouts

Analog and mixed-signal (AMS) circuit designs still rely on human design expertise. Machine learning has been assisting circuit design automation by replacing human experience with artificial intelligence. This paper presents TAG, a new paradigm of learning the circuit representation from layouts leveraging text, self-attention and graph. The embedding network model learns spatial information without manual labeling. We introduce text embedding and a self-attention mechanism to AMS circuit learning. Experimental results demonstrate the ability to predict layout distances between instances with industrial FinFET technology benchmarks. The effectiveness of the circuit representation is verified by showing the transferability to three other learning tasks with limited data in the case studies: layout matching prediction, wirelength estimation, and net parasitic capacitance prediction.Comment: Accepted by ICCAD 202

Estimating circuit delays in FPGAs after technology mapping

An FPGA implementation requires a significant effort of the hardware designer, who optimizes FPGA designs by going through many time-consuming CAD flow iterations. These iterations provide two types of feedback: (1) the FPGA performance and (2) the identification of the parts having the highest impact on the FPGA performance. Both depend on the wirelength behavior. Studies have been dedicated to the estimation of local [5] and global [4] wirelengths, but to our knowledge both performance estimations and identification of the critical zone are not present in literature. Therefore this paper, firstly, presents a comparison of three performance estimation techniques: logic depth, Monte Carlo simulation and fast placement (ordered from low to high accuracy and runtime). Secondly, four methods identifying the critical zone are compared. Results show that Monte Carlo simulations provide a good identification of the parts having the highest impact on the performance. We conclude that Monte Carlo simulations provide useful feedback within a short runtime (about 30 times faster than placement), reducing the time-to-market of FPGA implementations

The IPS fidelity scale as a guideline to implement Supported Employment

info:eu-repo/semantics/publishe

Netlist Decomposition and Candidate Generation for Analog IC Routing

Netlist decomposition and candidate generation is a non-conventional approach in the routing stage of the place and route (PnR) flow. While there has been significant research and advancement in the digital domain for automation with respect to this as well as other techniques, very little work has been done in the analog domain due to its complex constraints and specific requirements. With this proposed method, the most common requirements of Analog circuits are taken into consideration to provide candidate routes for netlists of analog Integrated Chips (IC). Netlist decomposition is an important stage of breaking down multi-pin nets into two-pin nets by adding additional nodes for each net. The proposed method takes into account blockages and constraints such as symmetry and bends to develop a new algorithm using Steiner trees and Hanan grids to generate optimal Steiner points. This method also breaks down multi-pin nets to 3-pin nets which reduces the wirelength and computations significantly. The decomposed net segments are run through Dijkstra algorithm to generate multiple candidates and an Integer Linear programming (ILP) solver is used to pick the best candidates that follow all the constraints and design rules. The experimental results show that overall wirelength is reduced by 5.16% while using 3-pin net decomposition when compared to 2-pin net decomposition. There is also a reduction in the number of metal layers used and the number of Steiner points generated. The method shows lesser computations when compared to other decomposition techniques as it avoids multiple reroutes to obtain Design Rule Check (DRC) clean routes

Netlist Decomposition and Candidate Generation for Analog IC Routing

Netlist decomposition and candidate generation is a non-conventional approach in the routing stage of the place and route (PnR) flow. While there has been significant research and advancement in the digital domain for automation with respect to this as well as other techniques, very little work has been done in the analog domain due to its complex constraints and specific requirements. With this proposed method, the most common requirements of Analog circuits are taken into consideration to provide candidate routes for netlists of analog Integrated Chips (IC). Netlist decomposition is an important stage of breaking down multi-pin nets into two-pin nets by adding additional nodes for each net. The proposed method takes into account blockages and constraints such as symmetry and bends to develop a new algorithm using Steiner trees and Hanan grids to generate optimal Steiner points. This method also breaks down multi-pin nets to 3-pin nets which reduces the wirelength and computations significantly. The decomposed net segments are run through Dijkstra algorithm to generate multiple candidates and an Integer Linear programming (ILP) solver is used to pick the best candidates that follow all the constraints and design rules. The experimental results show that overall wirelength is reduced by 5.16% while using 3-pin net decomposition when compared to 2-pin net decomposition. There is also a reduction in the number of metal layers used and the number of Steiner points generated. The method shows lesser computations when compared to other decomposition techniques as it avoids multiple reroutes to obtain Design Rule Check (DRC) clean routes