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LEGaTO: Machine Learning Use Case
The Legato project, funded by the EU's Horizon 2020 program, focuses on optimizing deep learning for automotive and IoT applications to enhance efficiency and reduce costs. Embedl, a spinoff from Chalmers University, offers an award-winning deep learning optimization engine that significantly improves energy efficiency and execution time. The project aims to alleviate the burden of manual experimentation for data scientists, allowing them to concentrate on core product development.
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LEGaTO: Machine Learning Use Case
- 1. The LEGaTO projecthas received funding from the European Union's Horizon 2020 research and innovation programme under the grant agreement No 780681 LEGaTO: Machine Learning Use Case AI4EU Café Webinar Hans Salomonsson CEO and co-founder of EmbeDL 28/October/2020
- 2. AI4EU Cafe Three enablersfor the current AI boom 1 2 3
- 3. AI4EU Cafe Applications inall industry segments Automotive Robotics & IoT Medical Devices Mobile Aviation/DefenceSmart HomeSmart Factory “BY YEAR 2035, WE EXPECT A TRILLION NEW IOT DEVICES” “IN 2022, 80% OF ALL IOT DEVICES WILL HAVE AI.” Automotive
- 4. AI4EU Cafe Self-driving Carshave an Energy Problem Life Cycle Assessment of Connected and Automated Vehicles: Sensing and Computing Subsystem and Vehicle Level Effects James H. Gawron, Gregory A. Keoleian, Robert D. De Kleine, Timothy J. Wallington, and Hyung Chul Kim Environmental Science & Technology 2018 52 (5), 3249-3256 Researchers at University of Michigan estimate that Autonomous Vehicles will increase energy consumption and green house gases by 3–20% due to increase in power consumption, weight, drag, and data transmission.
- 5. AI4EU Cafe Deep Learningexperiences exponential growth
- 6. AI4EU Cafe Lack ofcomparative studies Source: Blalock, D., Ortiz, J.J.G., Frankle, J. and Guttag, J., 2020. What is the State of Neural Network Pruning?. arXiv preprint arXiv:2003.03033.
- 7. AI4EU Cafe Deep Learningin Embedded Systems $$$• • • 11e6 parameters 2e9 FLOPs
- 8. AI4EU Cafe Deep Learningin Embedded Systems 1. ML IS A FAST GROWING FIELD AND DL OPTIMIZATION IS A HIGHLY SPECIALIZED NICHE, ORTHOGONAL TO MODEL DEVELOPMENT 2. NEED FOR SYSTEMATIC EVALUATION OF METHODS 3. NEED FOR OPTIMALLY COMBINING AND UNIFYING METHODS
- 9. Efficient Deep LearningIn Automotive and IoT
- 10. About Us Make DeepLearning more efficient to: ● meet real-time requirements ● reduce hardware cost ● reduce energy consumption by automated optimization of Deep Learning models. Spinoff from Chalmers University Gothenburg, Sweden Fast Growing Startup Anchorage Capital VC Backed 2017-2020 2020-2023 LV-EmbeDL2020-2021 Research Projects Networks
- 11. DEEP LEARNING CHALLENGES THEPROBLEM Developing deep learning models involves time consuming experimentation DEVELOPMENT PROCESS Deep Learning is computationally expensive making it hard to meet real time requirements REAL TIME REQUIREMENTS Your team needs to allocate optimization specialists, who don’t contribute to developing the core product OPTIMIZATION SPECIALISTS Deep Learning requires powerful hardware and high energy consumption EXPENSIVE HARDWARE
- 12. EmbeDL TECHNOLOGY Our awardwinning (HiPEAC) Deep Learning Optimization engine compresses any deep learning model to meet your requirements of ● Execution Time (Latency) ● Throughput ● Runtime Memory Usage ● Power Consumption DL Optimization Engine
- 13. BENEFITS USING EMBEDL PERFORMANCE COSTSAVINGS TIME TO MARKET FOCUS ON CORE PROBLEM EmbeDL will make sure that your team meets the product requirements by removing time consuming manual experimentation. Let EmbeDL take care of the optimization and your data scientists can focus on your company’s core activities and use their full capacity to develop market leading products. Model optimization frees up computational resources that instead can be used for higher sampling rate, increased sensor data or other system level improvements. Reduces hardware cost and energy consumption.
- 14. Benchmarks, Energy EmbeDL improvesenergy efficiency up to 11x compared to hardware vendor’s deep learning inference optimization tools.
- 15. Benchmarks, Execution Time EmbeDLimproves execution time up to 11x compared to hardware vendor’s deep learning inference optimization tools.
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- 17. ● EmbeDL hasbeen spun-out as a startup with private funding ● Our mission is to make Deep Learning efficient in Automotive and AIoT ● EmbeDL’s optimization approach bridges DL SW tools and HW platforms ● By using optimization by EmbeDL you can: ○ Reduce energy consumption ○ Reduce hardware costs ○ Free up time of internal resources to focus on the core problem ○ Faster time to market SUMMARY THE END
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