Enterprise AI Analysis
Accelerating graph substitutions in DNN optimization by heuristic algorithms
This paper introduces two heuristic methods, Memory-Augmented Search (MAS) and Simulated Annealing Search (SAS), to significantly accelerate graph substitution in Deep Neural Network (DNN) optimization. Addressing the time-consuming nature of traditional search-based methods in vast search spaces, MAS employs a greedy strategy with memory augmentation to avoid local optima, while SAS uses simulated annealing to probabilistically accept degraded solutions, broadening the search. Experimental results demonstrate that both methods drastically reduce optimization time from hours to seconds, achieving similar DNN computing performance to existing methods without significant compromise on inference performance. SAS, in particular, often outperforms MAS and TENSAT in optimization time.
Executive Impact at a Glance
Key performance indicators highlighting the potential enterprise-level benefits derived from this research.
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Speedup in Tbest (SAS vs TASO)
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Speedup in Ttotal (SAS vs TASO for BERT)
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Speedup in Ttotal (SAS vs TENSAT for NasRNN)
Deep Analysis & Enterprise Applications
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The paper focuses on leveraging heuristic search algorithms, specifically Memory-Augmented Search (MAS) and Simulated Annealing Search (SAS), to tackle the complexity of graph substitution in DNN optimization. These methods aim to find efficient solutions by exploring the search space more effectively than exhaustive search or purely greedy approaches.
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Max Tbest Speedup (SAS vs TASO)
Enterprise Process Flow
Initial Computation Graph
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Cost-Based Heuristic Search (MAS/SAS)
→
Optimized Computation Graph
Graph substitution is a core technique for optimizing DNN computation graphs. The challenge lies in the vast search space of possible substitutions. The proposed methods demonstrate superior performance in reducing the time required to find optimal or near-optimal graph configurations, significantly improving the efficiency of DNN deployment.
| Method | Key Features | Search Efficiency (Ttotal) | Inference Performance |
|---|---|---|---|
| TASO |
|
Hours (e.g., 17422s for Inception-v3) | Baseline |
| TENSAT |
|
Minutes (e.g., 33s for ENAS) | Good, sometimes better |
| MAS |
|
Seconds/Minutes (e.g., 14s for BERT) | Competitive |
| SAS |
|
Seconds (e.g., 3.4s for BERT) | Competitive, often best |
MAS employs a greedy strategy complemented by a memory-augmented heuristic. It prioritizes computation graphs with lower costs to continue the search but stores previously explored graphs. When trapped in a local optimum, it leverages this history to broaden the search scope, aiming to escape local minima and find better global solutions.
MAS: Escaping Local Optima
In a scenario optimizing a complex DNN, a purely greedy approach might quickly converge to a locally optimal computation graph. MAS, however, would identify this stagnation and, using its memory-augmented history, pivot to another promising, previously explored graph. This allows it to break free from the local trap and continue searching for a globally better solution, avoiding premature convergence and leading to a more optimized final graph structure.
SAS enhances the greedy approach by introducing a probabilistic element inspired by simulated annealing. It not only accepts performance-improving substitutions but also, with a certain probability (controlled by a 'temperature' parameter), accepts solutions that might temporarily degrade performance. This mechanism allows the algorithm to explore a wider range of the search space, significantly reducing the risk of getting stuck in local optima and accelerating convergence to global optima.
SAS: Probabilistic Exploration
Consider a DNN optimization task where a direct greedy step leads to a local optimum. SAS, with its simulated annealing component, might probabilistically accept a seemingly 'worse' graph configuration at an early stage. This seemingly counter-intuitive step could unlock a pathway to a much better, globally optimal solution that a purely greedy approach would never discover, ultimately yielding superior performance. This exploration of the search space is crucial for complex, high-dimensional problems.
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Your Phased Implementation Roadmap
A strategic overview of how we partner with enterprises to integrate cutting-edge AI for graph optimization.
Phase 1: Discovery & Assessment
Comprehensive analysis of existing DNN architectures, current optimization techniques, and performance bottlenecks to identify high-impact areas for MAS/SAS application.
Phase 2: Custom Model Adaptation
Tailoring MAS or SAS heuristics and substitution rule sets to your specific models and hardware, ensuring maximum efficiency gains and compatibility.
Phase 3: Integration & Testing
Seamless integration of the optimized graph substitution pipelines into your existing deep learning frameworks, followed by rigorous performance and inference accuracy testing.
Phase 4: Monitoring & Refinement
Continuous monitoring of deployed models for sustained performance and iterative refinement of optimization parameters to adapt to evolving computational landscapes.
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