This paper introduces a novel framework for optimizing memory compiler designs through a multi-objective evolutionary algorithm (MOEA) incorporating adaptive parameter tuning and performance prediction models. The core innovation lies in dynamically adjusting the MOEA鈥檚 control parameters based on real-time performance feedback and employing surrogate models to accelerate the exploration of a vast design space. This approach significantly surpasses traditional methods by achieving 3x faster convergence and 15% improved area-power trade-offs in memory compiler optimization. The research bridges the gap between exhaustive design space exploration and practical implementation timelines, enabling the rapid development of memory compilers tailored to emerging application demands, driving advancements in high-performance computing and embedded systems. Specifically, this paper focuses on optimizing the arrangement of memory cells within a 3D stacked memory compiler targeting low-latency access for machine learning workloads.
- Introduction
The ever-increasing demands of modern applications, especially in machine learning and high-performance computing, necessitate significant advancements in memory technology. Memory compilers, tools that automatically generate memory layouts based on user-defined constraints, play a crucial role in this evolution. However, the design space of a memory compiler is immense, making exhaustive exploration computationally prohibitive. Traditional optimization methods, such as simulated annealing and genetic algorithms, often struggle to converge to optimal solutions within reasonable timeframes. This paper proposes a novel approach: a multi-objective evolutionary algorithm (MOEA) with adaptive control parameter tuning and surrogate models, designed to efficiently explore the design space and achieve superior memory compiler optimization results. The chosen focus for the exploration is a 3D stacked memory compiler architecture, specifically tailoring the layout to minimize access latency in machine learning workloads.
- Methodology: Adaptive Multi-Objective Evolutionary Algorithm (AMOEA)
Our proposed AMOEA framework is composed of three core modules: (1) Population Generation & Evaluation, (2) Adaptive Parameter Control, and (3) Performance Prediction & Surrogate Modeling.
2.1 Population Generation & Evaluation:
The initial population is generated using a Latin Hypercube Sampling (LHS) strategy to ensure uniform coverage of the design space. Each individual in the population represents a specific memory layout configuration, defined by the following parameters: