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LEGaTO: Machine Learning Use Case

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Presentation by Hans Salomonsson at the webinar AI4EU WebCafé: 'Energy-efficient AI, a perspective from the LEGaTO project' on 28 October 2020

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LEGaTO: Machine Learning Use Case

  1. 1. The LEGaTO project has 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. 2. AI4EU Cafe Three enablers for the current AI boom 1 2 3
  3. 3. AI4EU Cafe Applications in all 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. 4. AI4EU Cafe Self-driving Cars have 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. 5. AI4EU Cafe Deep Learning experiences exponential growth
  6. 6. AI4EU Cafe Lack of comparative 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. 7. AI4EU Cafe Deep Learning in Embedded Systems $$$• • • 11e6 parameters 2e9 FLOPs
  8. 8. AI4EU Cafe Deep Learning in 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. 9. Efficient Deep Learning In Automotive and IoT
  10. 10. About Us Make Deep Learning 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. 11. DEEP LEARNING CHALLENGES THE PROBLEM 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. 12. EmbeDL TECHNOLOGY Our award winning (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. 13. BENEFITS USING EMBEDL PERFORMANCE COST SAVINGS 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. 14. Benchmarks, Energy EmbeDL improves energy efficiency up to 11x compared to hardware vendor’s deep learning inference optimization tools.
  15. 15. Benchmarks, Execution Time EmbeDL improves execution time up to 11x compared to hardware vendor’s deep learning inference optimization tools.
  16. 16. Demo
  17. 17. ● EmbeDL has been 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
  18. 18. THANK YOU Hans Salomonsson CEO hans@embedl.ai

Presentation by Hans Salomonsson at the webinar AI4EU WebCafé: 'Energy-efficient AI, a perspective from the LEGaTO project' on 28 October 2020

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