Enterprise AI Analysis
Syno: Structured Synthesis for Neural Operators
Our latest research introduces Syno, an end-to-end framework that automatically and efficiently discovers novel neural operators. Unlike traditional NAS or tensor compilers, Syno synthesizes operators at a fine granularity, leading to superior accuracy and execution performance. This breakthrough redefines how we optimize neural networks, offering significant speedups and efficiency gains on various hardware platforms without compromising model accuracy.
Accelerating Enterprise AI with Syno's Innovation
Syno's novel approach to neural operator synthesis delivers measurable, significant improvements across key performance indicators, enhancing both speed and efficiency for deep learning deployments.
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Average Speedup (Mobile CPU)
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Average Speedup (Torch.Compile)
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GPT-2 Perplexity
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Fewer FLOPs (ResNet-34)
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Redundancy Reduced
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Syno delivers significant performance improvements across various hardware, translating directly into faster inference and training for your AI models.
4.73x
Max Speedup on ImageNet (TVM)
Syno-optimized operators achieved up to 4.73x speedups on ImageNet, compiled with the TVM backend, demonstrating significant performance gains for large-scale vision models with only 1-2% accuracy loss. This represents a substantial leap in computational efficiency for demanding AI tasks.
70x
Redundancy Reduction
Syno's canonicalization rules significantly reduce search space redundancy, cutting more than 70x redundant candidates. This efficiency is critical for practical operator synthesis, allowing the framework to explore distinct and high-quality operators more effectively without re-evaluating semantically similar designs.
Discover the innovative end-to-end framework Syno uses to automatically synthesize efficient neural operators.
Enterprise Process Flow
Extract Operators from Model
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Synthesize Substitutions with Primitives
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Guided Search (MCTS + Shape Distance)
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Accuracy Evaluation (PyTorch)
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Performance Tuning (TVM/Torch.compile)
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Output Best Operators
Syno employs a structured, end-to-end workflow to synthesize neural operators. It starts by analyzing an existing model, then iteratively builds new operators using fine-grained primitives, guided by shape distance and canonicalization, and finally evaluates them for both accuracy and speed.
Understand how Syno surpasses existing methods like traditional NAS and tensor compilers by enabling true novel operator discovery.
| Feature | Syno (Structured Synthesis) | Traditional NAS | Tensor Compilers |
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| Operator Design Granularity | Fine-grained (from primitives) | Coarse-grained (composing existing ops) | Loop-level optimization of existing ops |
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| Redundancy Elimination |
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| Shape-Guided Search |
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Syno distinguishes itself by offering fine-grained operator synthesis, enabling the discovery of truly novel operators that go beyond the capabilities of traditional Neural Architecture Search (NAS) and tensor compilers. Its unique blend of structured search, canonicalization, and shape-guided synthesis provides a powerful new paradigm for AI optimization.
Explore specific examples of novel operators discovered by Syno, revealing insights into new, efficient AI algorithms.
Operator 1: A Novel Grouped Convolution Variant
Summary: Syno discovered 'OPERATOR 1', a convolution-like structure that behaves as a stacked two-stage grouped convolution. This operator achieved 2.68x, 2.04x, and 1.28x speedups on mobile CPU, mobile GPU, and A100 respectively, with less than 1% ImageNet accuracy degradation. It effectively reduces FLOPs and parameters through a unique combination of UNFOLD and SHARE primitives, leading to a highly efficient and performant design not discoverable by conventional NAS.
Details: The operator's pGraph (Figure 7 in the paper) shows a novel pattern involving two SHAREs, one REDUCE, and three coordinates with domain k1 in the first stage. This structure allows for a different receptive field compared to standard convolutions, potentially easing model training. The materialized reduction optimization (Section 8) further enhances its efficiency. This example highlights Syno's capability to generate operators with non-trivial, performance-boosting semantics.
Quantify Your Potential AI Savings
Use our interactive calculator to estimate the annual cost savings and reclaimed work hours your enterprise could achieve by optimizing AI operations with Syno's techniques.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Transformation Roadmap
Our proven phased approach ensures a smooth integration of advanced AI optimization techniques into your existing infrastructure.
Phase 1: Discovery & Strategy
Deep dive into your current AI architecture, identify key operators, and define optimization goals and success metrics. Establish a tailored strategy leveraging Syno's capabilities.
Phase 2: Operator Synthesis & Validation
Utilize Syno to synthesize novel, high-performance operators for your critical neural network components. Rigorous validation for accuracy and speed on your specific datasets.
Phase 3: Integration & Deployment
Seamlessly integrate optimized operators into your existing AI models. Leverage advanced tensor compilers for hardware-specific tuning and deploy for real-world performance gains.
Phase 4: Monitoring & Continuous Optimization
Implement robust monitoring to track performance and efficiency. Iterate on operator synthesis as your models evolve, ensuring sustained competitive advantage.
Ready to Optimize Your AI Performance?
Unlock the full potential of your neural networks with Syno's structured synthesis. Schedule a consultation to explore how our innovations can drive efficiency and accuracy for your enterprise.
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