Sequential Cell Banking for IC Power and Timing Optimization
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Solution Overview
Problem
Current integrated circuit design processes are labor-intensive and error-prone, particularly when dealing with large numbers of sequential cells, as they require manual placement and optimization for power and timing, leading to suboptimal results in routability and power consumption.
Innovation Solution
An automated sequential cell banking algorithm that identifies and groups sequential cells to minimize clock-tree power and susceptibility to variation by placing cells closer to the leaf-level clock-tree cell, reducing net capacitance and optimizing placement based on timing and congestion information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual placement and optimization is performed for sequential cells, then power and timing performance can be optimized, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The system automatically performs placement optimization by analyzing timing information and congestion data, with the placement tool itself identifying and banking sequential cells without requiring manual designer intervention. The automated algorithm evaluates timing slack and routing congestion to determine optimal cell grouping, making the system self-optimizing rather than requiring human expertise for each decision.
Solution Approach 2:
The manual mechanical process of designer review and adjustment is replaced by an automated computational algorithm that processes timing information and congestion data to automatically identify sequential cells for banking. The system uses computer-based analysis of placement data, timing constraints, and routing congestion to make placement decisions, substituting human manual optimization with automated mechanical processing.
2Productivity
If automated placement algorithms are used, then productivity is improved, but they fail to account for power consumption considerations
Solution Approach 1:
The automated placement algorithm applies different optimization criteria to different regions and cell types. Specifically, it identifies sequential cells based on local timing characteristics and congestion conditions, creating localized optimization zones where sequential cells are banded together when timing slack and routing congestion indicate benefit. This allows automated processing while achieving power optimization through localized adaptive decision-making.
Solution Approach 2:
The system changes the optimization parameters being used by the placement algorithm. Instead of optimizing solely for timing or routability, the algorithm incorporates power consumption as a key parameter, using timing information and congestion data to determine when sequential cell banking will reduce power consumption. This parameter expansion allows automated placement to simultaneously achieve productivity and power optimization.
3Loss of energy
If sequential cells are placed close to clock-tree cells to reduce net capacitance, then power consumption decreases, but routing congestion may increase
Solution Approach 1:
The algorithm applies partial banking of sequential cells rather than complete consolidation. It selectively identifies and banks only those sequential cells where timing slack and congestion data indicate benefit, rather than forcing all sequential cells into banks. This partial action approach reduces power consumption for critical paths while avoiding excessive routing congestion by leaving non-critical cells in their original locations.
Solution Approach 2:
The placement algorithm uses feedback from timing analysis and congestion data to iteratively refine sequential cell placement decisions. The system analyzes timing slack and routing congestion information, adjusts the placement of sequential cells accordingly, and repeats the analysis to converge on an optimal solution. This feedback mechanism allows the system to balance power reduction with congestion management by continuously adapting to the impact of placement changes.
Data Source
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AI summary
Various methods and apparatuses (such as computer readable media implementing the method) are described that relate to proximate placement of sequential cells of an integrated circuit netlist. For example, the preliminary placement is received; and based on the preliminary placement, a group of sequential cells is identified as being subject to improved power and/or timing upon subsequent placement. In another example, identification is received of a group of sequential cells subject to improved power and/or timing upon subsequent placement; and proximate placement is performed of the identified group of sequential cells. In yet another example, a proximate arrangement of a group of sequential cells is received; and if proximate placement fails, then the group of sequential cells is disbanded and placement is performed of the sequential cells of the disbanded group.