STAR: Synthesis of Tailored Architectures (2411.17800v1)

Abstract: Iterative improvement of model architectures is fundamental to deep learning: Transformers first enabled scaling, and recent advances in model hybridization have pushed the quality-efficiency frontier. However, optimizing architectures remains challenging and expensive. Current automated or manual approaches fall short, largely due to limited progress in the design of search spaces and due to the simplicity of resulting patterns and heuristics. In this work, we propose a new approach for the synthesis of tailored architectures (STAR). Our approach combines a novel search space based on the theory of linear input-varying systems, supporting a hierarchical numerical encoding into architecture genomes. STAR genomes are automatically refined and recombined with gradient-free, evolutionary algorithms to optimize for multiple model quality and efficiency metrics. Using STAR, we optimize large populations of new architectures, leveraging diverse computational units and interconnection patterns, improving over highly-optimized Transformers and striped hybrid models on the frontier of quality, parameter size, and inference cache for autoregressive LLMing.

• The paper introduces STAR, a novel evolutionary framework that uses LIV-based hierarchical genome encoding to optimize deep learning architectures beyond traditional methods.
• It leverages a three-level search space—featurization, operator structure, and backbone—to systematically refine diverse model architectures.
• Empirical results show up to a 13% reduction in parameters and a 90% reduction in cache size, achieving significant efficiency gains without compromising performance.

Synthesis of Tailored Architectures: A Methodological Advancement in Automated Model Optimization

The paper "STAR: Synthesis of Tailored Architectures" presents a novel approach to the automated optimization of deep learning architectures. The central theme revolves around the challenges of iteratively refining model architectures, which historically have relied on Transformers and model hybridization. The authors propose the Synthesis of Tailored Architectures (STAR) as a solution that leverages a new design space based on linear input-varying systems (LIVs) and hierarchical encoding to optimize model architectures using gradient-free, evolutionary techniques.

Overview and Methodology

The paper identifies two primary paths in model architecture improvement: automated and manual design. While existing automated approaches primarily focus on convolutional networks and basic computational primitives, they lack a unified framework that transcends domains and objectives. STAR introduces a hierarchical search space grounded in the LIV theory, allowing for the representation and optimization of diverse architectures through architecture genomes. These genomes are optimized using evolutionary algorithms, refining designs for various quality and efficiency metrics.

The hierarchical design space in STAR is noteworthy for its reliance on LIV operators that generalize common computational units, such as attention, recurrences, and convolutions. The authors describe a three-level hierarchical resolution: (a) featurization, (b) operator structure, and (c) backbone, which enables the comprehensive and well-conditioned search space necessary for identifying novel model architectures.

The encoding into architecture genomes allows STAR to implement evolutionary techniques, including assessment, pairing, recombination, and mutation, systematically optimizing architecture parameters. This aligns with the goals of achieving improvements across multiple objectives, such as model size, inference cache, and perplexity during autoregressive LLMing.

Key Findings

The empirical results demonstrate STAR's potential. When optimized for quality and efficiency metrics, the synthesized architectures outperform traditional and hybrid Transformer models. Specifically, architectures evolved through STAR achieve up to 13% reduction in parameter counts while maintaining comparable or superior predictive performance. Similarly, cache size reductions of up to 90% are achieved with negligible impact on quality, evidencing substantial gains in efficiency.

The paper reports consistent performance improvements using STAR across various model sizes and configurations. Significantly, the optimized architectures highlight recurring motifs that contribute to observed performance enhancements, showcasing the framework's ability to discover novel design patterns autonomously.

Implications and Future Directions

The introduction of STAR extends the automated synthesis of model architectures beyond conventional methods. The framework provides a robust tool for developing models tailored to specific applications, considering diverse demands like efficiency and quality. The scalability of STAR, demonstrated by consistent improvements even in larger models, suggests its potential applicability across a broader range of domains in AI.

Future research can capitalize on the modular structure of the LIV framework to incorporate even more complex computational units. Moreover, the hierarchical genome representation could be further refined to enhance the specificity and granularity of evolutionary optimizations. The exploration of different evolutionary algorithms and architectural motifs might yield new insights into architecture design, contributing to more effective and efficient AI systems.

In conclusion, the STAR framework represents a methodological advancement in architectural optimization for deep learning, providing a comprehensive, automated solution that promises significant gains in model efficiency and quality.