Parallel Prefix Data Path Optimization Using Reinforcement Learning
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Solution Overview
Problem
Conventional methods for designing data path circuits, such as parallel prefix circuits, are ineffective in optimizing area, delay, and power consumption due to physical design complexities and reliance on inaccurate analytical models and heuristic rules, which fail to scale for physical synthesis.
Innovation Solution
Utilizing reinforcement learning with a machine learning model to modify and optimize the design of data path circuits by adding or removing nodes in the prefix graph, training the model to minimize area, power consumption, and delay through a reward-based system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional analytical models and heuristic rules are used for data path circuit design, then the design process is simple and fast, but the optimization of area, delay, and power consumption is ineffective
Solution Approach 1:
The patent replaces conventional analytical models and heuristic rules with a machine learning model trained through reinforcement learning. This substitution enables the system to learn optimal circuit configurations from training data, achieving superior optimization of area, delay, and power consumption without relying on inaccurate analytical approximations or manual heuristic rules.
Solution Approach 2:
The patent changes the fundamental approach from using fixed analytical models to using a trained machine learning model that can adapt its predictions based on learned patterns. The ML model takes circuit parameters as input and outputs optimized configurations, dynamically adjusting design decisions based on the specific problem instance rather than applying universal heuristics.
2Manufacturing precision
If exhaustive search approaches are used to optimize prefix circuits, then complete optimization can be achieved for small input lengths, but the method does not scale beyond small input lengths
Solution Approach 1:
The patent performs preliminary training of the machine learning model on a comprehensive dataset of circuit designs before deployment. During this training phase, the model learns from numerous examples including exhaustive search results for small inputs, acquiring the ability to generalize to larger input lengths without requiring exhaustive search at runtime. This preliminary learning enables the model to make accurate optimization decisions for scalable problem sizes.
Solution Approach 2:
The patent uses the machine learning model to copy and generalize patterns learned from training examples to new, larger problem instances. Instead of performing exhaustive search for each new circuit design, the model replicates the optimization insights gained during training, enabling scalable optimization without repeating the computationally expensive exhaustive search process.
3Ease of operation
If regular prefix circuit structures are proposed with fixed trade-offs, then design simplicity is maintained, but the ability to optimize area, power, and delay is limited due to physical design complexities
Solution Approach 1:
The patent replaces fixed regular prefix circuit structures with machine learning-generated designs that account for physical implementation details. The ML model learns the relationship between logical circuit structures and their physical implementations, including capacitive loading and congestion effects, enabling optimization that reflects actual physical performance rather than idealized logical properties.
Solution Approach 2:
The patent enables different parts of the circuit to have optimized properties tailored to their specific functions and physical contexts. Rather than applying uniform regular structures throughout, the ML model generates locally optimized configurations that adapt to specific area, power, and delay requirements of different circuit regions, accounting for variations in capacitive loading and congestion across the design.
Data Source
AI summary
Apparatuses, systems, and techniques for designing a data path circuit such as a parallel prefix circuit with reinforcement learning are described. A method can include receiving a first design state of a data path circuit, inputting the first design state of the data path circuit into a machine learning model, and performing reinforcement learning using the machine learning model to output a final design state of the data path circuit, wherein the final design state of the data path circuit has decreased area, power consumption and/or delay as compared to conventionally designed data path circuits.