Enterprise AI Analysis
Modulation Recognition in a System-on-Chip
This research presents a novel FPGA-based dual accelerator design for modulation recognition systems, optimized for low latency and power in edge AI hardware. Demonstrating over 8x performance gains and 1.35x power savings, this approach revolutionizes real-time signal processing.
Key Enterprise Impact
The specialized accelerator design significantly boosts performance and efficiency, offering substantial benefits for real-time edge AI applications.
8.22x
Overall Application Performance Speedup
0
Application Speedup
0
Power Savings
0
SSCA Accelerator Speedup
System-on-Chip Implementation Flow
Develop Specialized Accelerator IP
→
HLS for ML (ResNet)
→
RTL for DSP (SSCA)
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Integrate to SoC (ESP)
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FPGA-based Dual Accelerator
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Achieve Low Latency & Power MR
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
SoC Design for Modulation Recognition
The research focuses on integrating specialized accelerator intellectual property (IP) for Modulation Recognition (MR) into a System-on-Chip (SoC) environment. This leverages the Embedded Scalable Platforms (ESP) framework to effectively combine heterogeneous accelerators.
The primary goal is to optimize for low latency and power efficiency in edge Artificial Intelligence (AI) hardware applications, crucial for real-time signal processing.
HLS, RTL, and FPGA Acceleration
The system utilizes a dual-accelerator design on a Field-Programmable Gate Array (FPGA):
- Residual Network (ResNet) Accelerator: Implemented using High-Level Synthesis (HLS), ideal for machine learning algorithms.
- Strip Spectral Correlation Algorithm (SSCA) Accelerator: Manually coded at Register-Transfer Level (RTL) for precise control over signal processing tasks.
This hybrid approach combines the rapid development of HLS with the fine-tuned optimization of RTL for different computational needs.
FPGA vs. Conventional Software Performance
The FPGA-based dual accelerator design significantly outperforms software implementations. Key findings include:
- Overall application execution speedup exceeding 8x.
- Demonstrated power savings of approximately 1.35x compared to conventional hardware.
- Specifically, the SSCA Accelerator achieved an impressive 29.59x speedup, highlighting the benefits of dedicated hardware for computationally intensive signal processing.
See the detailed timing comparison table below for specific operational metrics.
| Operation | Orin (s) | FPGA (s) | Speedup (x) |
|---|---|---|---|
| Application | 44.96 | 5.47 | 8.22 |
| Resnet | 14.00 | 2.38 | 5.87 |
| Accelerator (ResNet) | 10.96 | 1.46 | 7.50 |
| SSCA | 30.96 | 4.01 | 7.73 |
| Accelerator (SSCA) | 30.17 | 1.02 | 29.59 |
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Our Proven Implementation Roadmap
Our phased approach ensures a smooth transition and maximal impact for your enterprise AI initiatives, mirroring proven methodologies.
Phase 1: Discovery & Strategy
Collaborate to define project scope, objectives, and success metrics. Analyze existing infrastructure and data sources to tailor the optimal SoC and accelerator design strategy, leveraging insights from the latest research.
Phase 2: Design & Prototyping
Develop custom accelerator IP (HLS/RTL) and integrate into an ESP framework-based SoC. Create high-fidelity prototypes on FPGA, focusing on initial performance validation and iterative refinement based on real-world data.
Phase 3: Development & Integration
Full-scale development of the chosen SoC solution. Rigorous testing and optimization for latency and power efficiency. Seamless integration into your existing enterprise systems, ensuring compatibility and scalability.
Phase 4: Deployment & Optimization
Controlled rollout and deployment of the optimized MR system. Continuous monitoring, performance tuning, and post-deployment support to ensure long-term stability and to unlock further operational efficiencies.
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