Enterprise AI Analysis
Adapting scientific streaming inference workflows for a deterministic tensor processing unit
This paper proposes a hybrid hardware solution for real-time X-ray data processing, integrating an FPGA with a Groq AI accelerator. This system enables low-latency, high-throughput inference by streaming data directly to the Groq accelerator. Key findings include a 3.6x speedup over previous systems for a single 128x128 image inference, completing in 156.06 µs (including transfer time), and demonstrating the viability of edge computing for photon science experiments.
Executive Impact: Unleashing Real-time Inference
The integration of Groq AI accelerators with FPGAs significantly enhances processing capabilities for high-throughput, low-latency scientific workflows. This hybrid approach delivers tangible improvements across critical performance metrics, setting a new standard for real-time data analysis at the edge.
0x
Speedup over previous systems
0 µs
Per Inference (128x128)
0 kHz
Processing Rate (X-ray images)
Deep Analysis & Enterprise Applications
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Hybrid Architecture
Groq Accelerator Advantages
Performance Gain
The system utilizes a hybrid hardware approach, integrating an FPGA as the front-end processor for data acquisition, formatting, and initial filtering, alongside a Groq AI accelerator for computationally demanding tasks like pattern recognition and high-resolution image interpretation. This division significantly enhances computational capacity while maintaining low latency.
The GroqCard accelerator features a Tensor Streaming Processor (TSP) with 230 MB of SRAM, enabling high-bandwidth, low-latency memory access. Its architecture, with superlanes and SIMD units, is designed for deterministic, scalable performance, particularly advantageous for image processing, performing 409,600 INT8 operations per cycle. It significantly reduces overhead by routing traffic directly, bypassing PCIe and the CPU.
For a 128x128 image inference, including image transfer, the system achieves completion in 156.06 µs, supporting approximately 6.4 kHz processing with the edgePtychoNN model. This represents a 3.6x speedup over previous GPU-based systems (e.g., RTX A6000: 370 µs, GroqCard: 102.5 µs inference time). Quantization to 8-bit integers, while slightly impacting accuracy, significantly improves performance.
Enterprise Process Flow
X-ray Data Capture (Detector ASIC)
→
Data Streaming to FPGA
→
FPGA Preprocessing & Formatting
→
Data Transfer to Groq Accelerator (200G QSFP56)
→
Groq AI Inference (edgePtychoNN)
→
Results Feedback & Display
| Platform | Inference Latency (µs) | Key Advantages |
|---|---|---|
| GroqCard | 102.5 |
|
| NVIDIA RTX A6000 | 370 |
|
| NVIDIA AGX Xavier | 2300 |
|
Advanced ROI Calculator
Estimate the potential operational savings and efficiency gains for your enterprise by adopting advanced AI inference at the edge, similar to the Groq-FPGA hybrid system. Input your team size, average hours spent on data processing, and hourly rate to see the projected annual savings and reclaimed hours based on industry-specific efficiency multipliers.
Your AI Implementation Roadmap
A structured approach to integrating advanced AI inference into your enterprise workflows, from architectural design to future scalability.
Phase 1: Architecture Design & Quantization
Define hybrid FPGA-Groq architecture, integrate detector hardware, and perform 8-bit quantization of AI models like edgePtychoNN while evaluating accuracy against full-precision baseline.
Phase 2: System Integration & Performance Benchmarking
Implement data streaming (QSFP), Groq compiler optimization, and conduct inference performance benchmarks, including execution time characterization and comparison with GPU-based systems.
Phase 3: Real-time Deployment & Optimization
Deploy the system for real-time X-ray data processing, optimize communication latency, and refine overall computational capacity to achieve target throughput (e.g., 6.4 kHz processing).
Phase 4: Future Enhancements & Scalability
Explore NVLink Fusion for direct FPGA-GPU communication, investigate hybrid accelerator scheduling, and develop improved toolchains for wider applicability and future detector technologies.
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