Proceedings of the 2022 International Symposium on Physical Design
In a typical RTLtoGDSII flow, floorplanning plays an essential role in achieving decent quality o... more In a typical RTLtoGDSII flow, floorplanning plays an essential role in achieving decent quality of results (QoR). A good floorplan typically requires interaction between the frontend designer, who is responsible for the functionality of the RTL, and the backend physi cal design engineer. The increasing complexity of macrodominated designs (especially machine learning accelerators with autogener ated RTL) has made the floorplanning task even more challenging and timeconsuming. In this paper, we propose RTLMP, a novel macro placer which utilizes RTL information and tries to "mimic" the interaction between the frontend RTL designer and the back end physical design engineer to produce humanquality floorplans. By exploiting the logical hierarchy and processing logical modules based on connection signatures, RTLMP can capture the dataflow inherent in the RTL and use the dataflow information to guide macro placement. We also apply autotuning [37] to optimize hyperparam eter settings based on input designs. We have built RTLMP based on OpenROAD infrastructure 49] and applied RTLMP to a set of industrial designs. RTLMP outperforms stateoftheart commer cial macro placers and achieves QoR similar to that of handcrafted floorplans. • Hardware → Electronic design automation; Physical design (EDA); Partitioning and floorplanning.
Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple ... more State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinements on each level of the hierarchy. Multilevel partitioners are subject to two limitations: (i) Hypergraph coarsening processes rely on local neighborhood structure without fully considering the global structure of the hypergraph. (ii) Refinement heuristics can stagnate on local minima. In this paper, we describe SpecPart, the first supervised spectral framework that directly tackles these two limitations. SpecPart solves a generalized eigenvalue problem that captures the balanced partitioning objective and global hypergraph structure in a low-dimensional vertex embedding while leveraging initial high-quality solutions from multilevel partitioners as hints. SpecPart further constructs a family of trees from the vertex embedding and partitions them with a tree-sweeping algorithm. Then, a novel overlay of multiple tree-based partitioning solutions, followed by lifting to a coarsened hypergraph, where an ILP partitioning instance is solved to alleviate local stagnation. We have validated SpecPart on multiple sets of benchmarks. Experimental results show that for some benchmarks, our SpecPart can substantially improve the cutsize by more than 50% with respect to the best published solutions obtained with leading partitioners hMETIS and KaHyPar. CCS CONCEPTS • Hardware → Physical design (EDA); • Theory of computation → Design and analysis of algorithms.
Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
This paper describes new elements in the RDF-2022 release of the DATC Robust Design Flow, along w... more This paper describes new elements in the RDF-2022 release of the DATC Robust Design Flow, along with other activities of the IEEE CEDA DATC. The RosettaStone initiated with RDF-2021 has been augmented to include 35 benchmarks and four open-source technologies (ASAP7, NanGate45 and SkyWater130HS/HD), plus timingsensible versions created using path-cutting. The Hier-RTLMP macro placer is now part of DATC RDF, enabling macro placement for large modern designs with hundreds of macros. To establish a clear baseline for macro placers, new open-source benchmark suites on open PDKs, with corresponding flows for fully reproducible results, are provided. METRICS2.1 infrastructure in OpenROAD and OpenROAD-flow-scripts now uses native JSON metrics reporting, which is more robust and general than the previous Python scriptbased method. Calibrations on open enablements have also seen notable updates in the RDF. Finally, we also describe an approach to establishing a generic, cloud-native large-scale design of experiments for ML-enabled EDA. Our paper closes with future research directions related to DATC's efforts. CCS CONCEPTS • Hardware → Physical design (EDA); Methodologies for EDA.
Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD
Parameterizable ML accelerators are the product of recent breakthroughs in machine learning (ML).... more Parameterizable ML accelerators are the product of recent breakthroughs in machine learning (ML). To fully enable the design space exploration, we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It employs a unified methodology, coupling backend power, performance and area (PPA) analysis with frontend performance simulation, thus achieving realistic estimation of both backend PPA and system metrics (runtime and energy). Experimental studies show that the approach provides excellent predictions for both ASIC (in a 12nm commercial process) and FPGA implementations on the VTA and VeriGOOD-ML platforms. CCS CONCEPTS • Hardware → Physical design (EDA); Hardware accelerators; Application specific integrated circuits.
The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the ... more The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test.
Proceedings of the 2022 International Symposium on Physical Design
In a typical RTLtoGDSII flow, floorplanning plays an essential role in achieving decent quality o... more In a typical RTLtoGDSII flow, floorplanning plays an essential role in achieving decent quality of results (QoR). A good floorplan typically requires interaction between the frontend designer, who is responsible for the functionality of the RTL, and the backend physi cal design engineer. The increasing complexity of macrodominated designs (especially machine learning accelerators with autogener ated RTL) has made the floorplanning task even more challenging and timeconsuming. In this paper, we propose RTLMP, a novel macro placer which utilizes RTL information and tries to "mimic" the interaction between the frontend RTL designer and the back end physical design engineer to produce humanquality floorplans. By exploiting the logical hierarchy and processing logical modules based on connection signatures, RTLMP can capture the dataflow inherent in the RTL and use the dataflow information to guide macro placement. We also apply autotuning [37] to optimize hyperparam eter settings based on input designs. We have built RTLMP based on OpenROAD infrastructure 49] and applied RTLMP to a set of industrial designs. RTLMP outperforms stateoftheart commer cial macro placers and achieves QoR similar to that of handcrafted floorplans. • Hardware → Electronic design automation; Physical design (EDA); Partitioning and floorplanning.
Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple ... more State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinements on each level of the hierarchy. Multilevel partitioners are subject to two limitations: (i) Hypergraph coarsening processes rely on local neighborhood structure without fully considering the global structure of the hypergraph. (ii) Refinement heuristics can stagnate on local minima. In this paper, we describe SpecPart, the first supervised spectral framework that directly tackles these two limitations. SpecPart solves a generalized eigenvalue problem that captures the balanced partitioning objective and global hypergraph structure in a low-dimensional vertex embedding while leveraging initial high-quality solutions from multilevel partitioners as hints. SpecPart further constructs a family of trees from the vertex embedding and partitions them with a tree-sweeping algorithm. Then, a novel overlay of multiple tree-based partitioning solutions, followed by lifting to a coarsened hypergraph, where an ILP partitioning instance is solved to alleviate local stagnation. We have validated SpecPart on multiple sets of benchmarks. Experimental results show that for some benchmarks, our SpecPart can substantially improve the cutsize by more than 50% with respect to the best published solutions obtained with leading partitioners hMETIS and KaHyPar. CCS CONCEPTS • Hardware → Physical design (EDA); • Theory of computation → Design and analysis of algorithms.
Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
This paper describes new elements in the RDF-2022 release of the DATC Robust Design Flow, along w... more This paper describes new elements in the RDF-2022 release of the DATC Robust Design Flow, along with other activities of the IEEE CEDA DATC. The RosettaStone initiated with RDF-2021 has been augmented to include 35 benchmarks and four open-source technologies (ASAP7, NanGate45 and SkyWater130HS/HD), plus timingsensible versions created using path-cutting. The Hier-RTLMP macro placer is now part of DATC RDF, enabling macro placement for large modern designs with hundreds of macros. To establish a clear baseline for macro placers, new open-source benchmark suites on open PDKs, with corresponding flows for fully reproducible results, are provided. METRICS2.1 infrastructure in OpenROAD and OpenROAD-flow-scripts now uses native JSON metrics reporting, which is more robust and general than the previous Python scriptbased method. Calibrations on open enablements have also seen notable updates in the RDF. Finally, we also describe an approach to establishing a generic, cloud-native large-scale design of experiments for ML-enabled EDA. Our paper closes with future research directions related to DATC's efforts. CCS CONCEPTS • Hardware → Physical design (EDA); Methodologies for EDA.
Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD
Parameterizable ML accelerators are the product of recent breakthroughs in machine learning (ML).... more Parameterizable ML accelerators are the product of recent breakthroughs in machine learning (ML). To fully enable the design space exploration, we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It employs a unified methodology, coupling backend power, performance and area (PPA) analysis with frontend performance simulation, thus achieving realistic estimation of both backend PPA and system metrics (runtime and energy). Experimental studies show that the approach provides excellent predictions for both ASIC (in a 12nm commercial process) and FPGA implementations on the VTA and VeriGOOD-ML platforms. CCS CONCEPTS • Hardware → Physical design (EDA); Hardware accelerators; Application specific integrated circuits.
The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the ... more The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test.
Uploads
Papers by Zhiang Wang