Case Study
Bosch Accelerates Deep Learning Model Performance with Embedl SDK
Introduction
Optimizing AI Models Across Diverse Hardware Platforms
Bosch, a global leader in technology and engineering, partnered with Embedl to enhance the performance of its proprietary deep learning model using Embedl’s Model Optimization SDK. The goal: optimize the model for multiple hardware targets, specifically, AI accelerators from different vendors. The results were remarkable. Bosch engineers achieved substantial performance gains while preserving model accuracy, all without exposing proprietary data or model architecture. This case study showcases how skilled engineers, equipped with Embedl’s powerful tools, can unlock next-level performance across diverse hardware platforms.
Pilot Project Goals
The pilot project was designed with a clear objective:
Optimize Bosch’s deep learning model for multiple hardware platforms to improve inference speed and efficiency without compromising accuracy.
Key milestones included:
- Initial optimization for an existing hardware platform.
- Re-optimization for a second hardware target using a different toolchain.
- Enabling engineers to evaluate and choose the optimal target for deployment.
Step 1: Initial Optimization for Existing Hardware
The first phase focused on optimizing an interior sensing model for an existing hardware platform. Within just one week of receiving Embedl’s SDK, Bosch engineers successfully cut model latency in half.
This rapid progress was made possible by advanced model optimization techniques built into the SDK:
- Hardware-Aware Neural Architecture Search (NAS): Automatically tailors network architectures to the hardware’s specific constraints.
- Hardware-Aware Pruning: Removed high-latency redundant parameters while maintaining performance.
- Mixed Precision Quantization: Balanced speed and memory usage through precision tuning.
Bosch engineers handled the optimization independently, demonstrating the SDK’s ease of use. At no point did Embedl require access to Bosch’s original models or data, preserving data security and IP integrity.
Step 2: Re-Optimization for a Second Hardware Target
In the second phase, the optimized model was re-targeted to a different hardware platform with a distinct toolchain. This step tested Embedl’s SDK in terms of cross-platform flexibility.
Bosch engineers were able to:
- Seamlessly re-optimize the model for a new AI accelerator architecture.
- Recreate performance gains from the first optimization.
- Confirm the SDK’s ability to adapt across diverse hardware environments.
This step proved especially valuable for real-world deployment, where models often need to operate on varied hardware.
Key Benefits for Bosch
- Significant Speedup: The optimized models showed drastically reduced latency - ideal for real-time applications.
- Flexibility in Deployment: Multi-platform optimization allowed for strategic hardware decisions and potential cost savings.
- Ease of Use: Bosch engineers handled the entire optimization process autonomously, with minimal onboarding.
- Maintained Accuracy: Performance gains came without sacrificing accuracy, ensuring dependable results in the field.
Conclusion & Future Outlook
This successful collaboration between Bosch and Embedl illustrates the transformative potential of advanced model optimization. With no compromise in model accuracy and significant improvements in speed across different hardware platforms, Bosch now has a powerful edge in deploying efficient, real-time deep learning systems.
The results of this pilot project were presented at STARTUP AUTOBAHN EXPO2024, showcasing how Embedl’s Model Optimization SDK empowers engineers to achieve exceptional performance with minimal friction. You can watch the presentation here: https://www.embedl.com/news/pilot-presentation-bosch-embedl.
Looking ahead, Embedl continues to push the boundaries of model optimization—supporting industry leaders like Bosch in scaling AI performance across platforms, applications, and industries.