Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Company Profile LeapMind Inc. / Careers(EN)

7295ba1bb15719deb91f2acbc57bee65?s=47 LeapMind Inc.
January 22, 2019

Company Profile LeapMind Inc. / Careers(EN)


LeapMind Inc.

January 22, 2019


  1. LeapMind Inc. Company Introduction 2021.7

  2. Contents About us Business overview Members&Organization Work style 2 1

    2 3 4
  3. 1.About us 3

  4. To create innovative devices with machine learning and make them

    available everywhere MISSION 4 To provide key technologies to bring next-generation information devices into reality VISION
  5. Message Society is changing every minute with the evolution of

    technology. With breakthrough innovations in devices and widespread use of IT infrastructure, the use of data and Machine Learning are becoming more and more familiar to us. We were among the first to predict such a future. I've been running a machine learning based business since 2012. Since we are now working diligently with our colleagues with diverse skills to meet the needs of our customers, we have sought out the following two approaches; "Development of good machine learning models" and "Development of high-speed, highly efficient hardware IP". By approaching from both software and hardware perspectives, we will make the impossible possible. That future is within our reach. We believe that we can enrich people's lives by developing the key technologies of the future and make them available to the world. CEO Soichi Matsuda 5
  6. 6 Company Established Capital Main Investors Employees LeapMind Inc. 2012.12.25

    2,587 million yen (Including capital reserver) (As of March 31 2020) Itochu Technology Ventures, Intel Capital, Mitsui & Co., Ltd., Toyota Motor Corporation, SBI 65 Corporate Profile
  7. As a Series C About 3.5 billion JPY of funds

    raised 2019.10 2020.4 Keynote speaker at COOL Chips23 Ultra low power AI inference accelerator IP official launch「Efficiera®」for commerce 10 7 2012.12 Established LeapMind Corporation (formerly AddQuality Corporation) DeLTA-Lite released, "DeLTA-Project" announced 2018.4 Launch of the Efficiera FPGA Partner Program Blueoil, a software stack for quantization neural networks, released as open source software 12
  8. 8 Since its inception, we are providing services and products

    that are unique to the practical application of machine learning. From LeapMind's Inception to the Present Practical applications of machine learning technology Joint Development Operation Extremely low bit quantization Commercial IP OSS 2012創業 2018 2019 2020 Embedded DL development web service
  9. Weekly Toyo Keizai Forbes Japan Bloomberg

  10. 10 Cumulative total of funds raised: approx. 5 billion yen

    As a Series B Oct.2017 As a Series A Aug.2016 As a Series C Oct.2019 ITOCHU Technology Ventures, Inc. Visionnaire Ventures Fund Archetype Ventures Intel Capital GMO VenturePartners, Inc. NTT DATA Corporation Innovative Venture Fund Investment Limited Partnership ITOCHU Technology Ventures, Inc. Visionnaire Ventures Fund Archetype Ventures Aioi Nissay Dowa Insurance Co., Ltd. SBI Investment Co., Ltd. Toyota Motor Corporation MITSUI & CO., LTD. 3.5 billion JPY LeapMind has raised a total of approximately 5 billion yen in funding to date. About 1.15 billion JPY About 340 million JPY About
  11. 11 Experience in joint development with partners in a wide

    range of industries and more LeapMind has a track record of more than 150 AI co-development projects using edge machine learning technologies.
  12. Board Member 12 CEO Soichi Matsuda In 2011, he launched

    a matching service for engineers and clients based on engineering skill visualization, which was expanded to Singapore, and got his business acquired. He founded LeapMind to create a platform of "compact and simple" deep learning technologies that is easily accessible to anyone, thereby contributing to our society and advancing the world. CTO Hiroyuki Tokunaga He completed his master's program at the University of Tokyo Graduate School of Information Science and Engineering in 2007. Prior to becoming a Director and the CTO of LeapMind in 2018, he worked for Yahoo Japan Corporation, Preferred Infrastructure, Inc., and Smart News, Inc. Chief Research Officer & Chief Scientist Atsunori Kanemura, Ph.D. He received the Ph.D. in Informatics from Kyoto University, Japan. He has held positions at research institutions both in Japan and overseas, and his publications include more than 50 papers, 100 presentations, and a tutorial at AAAI, a flagship academic conference on artificial intelligence. Dr. Kanemura joined LeapMind as an Executive Officer and the Chief Research Officer in 2018 to show the future of intelligent machines embedded in various places of our society. VP of Business Katsutoshi Yamazaki Born in 1970. He holds a master's degree from Keio University and has held business management positions in semiconductor and IP manufacturing in Japan, the U.S. and Europe. He joined LeapMind in 2020 and is responsible for the Efficiera business.
  13. 2.Business Overview 13

  14. 14 Focus on edge deep learning LeapMind mainly has been

    collaborating with the number of projects in the field of image recognition of "deep learning" which is the element technology of AI.In particularly, we are focusing on research and development of edge deep learning, which allows deep learning to run on edge devices. AI Machine Learning Neural Network Deep Learning Edge Deep Learning
  15. 15 Expansion of the Edge Deep Learning Market As it

    is expected that the number of IoT devices steadily increases and the amount of data they handle explodes in the next few years, thus the market for edge deep learning is also expected to expand Global IoT Device Number and Forecast (Billion units)
  16. 16 Features of Edge Devices Issues with Cloud Computing Issues

    with GPU Embedding FPGA・ASIC Constant internet connection is not required Cheap device unit price Fast response Low power consumption High device unit price High power consumption Requires the internet Slow response High usage fee
  17. 17 Digital still camera Digital TV Drone solution Surveillance camera

    The main target market is area where power-saving and edge solutions are required such as automobiles and security, in short, where demand for edge deep learning is increasing Developing business by targeting low-power edge devices Healthcare Automotive Printer Industrial Agricultural / Construction machinery Smartphone Gaming
  18. Advantages of Edge Deep Learning Edge deep learning has advantages

    not found in cloud or GPU-based inference processing. It is expected to be used in situations where real-time responses are required even in the network environment is not stable or personal information is handled. Independent from bandwidth, latency and reliability of Internet connection Reduces security risks as no external transmission is required Reduces costs of uploading data and using cloud 18
  19. 19 The Strengths of LeapMind Ultra low power AI inference

    accelerator IP Efficiera Knowledge gained from many projects & support system We are developing our business with our original weight reduction technology for deep learning models, the dedicated circuit design and leveraging the knowledge gained from collaboration with more than 150 companies. Quantization technology in deep learning
  20. 20 Issue resolution support by LeapMind LeapMind can provide appropriate

    supports for challenges in each flow of AI development. Joint planning of Al projects Efficiera FPGA Partner Program Efficiera accelerator IP everaging extremely low bit quantization Knowledge gained through numerous projects Choosing the right Al solution Practical use, mass production
  21. LeapMind's core technology Extremely Low Bit Quantization 21

  22. 22 Challenges of Edge Deep Learning Edge deep learning has

    several challenges and barriers to practical application. limited computing resources Trade-off between computation and speed Limited processing power, electric power, and other computational resources available on the device With limited computational resources, there is a trade-off between the amount of computation and the processing speed of inference
  23. • Extreme quantization reduces logic and memory usage. • Channel

    doubling technique can recover accuracy. 23 One of the methods of weight reduction for deep learning models, "quantization" that has been pushed to the limit. It leads to solutions for edge deep learning issues such as computational complexity. Solution : Extremely Low Bit Quantization
  24. 24 Extremely Low Bit Quantization Applying extremely low bit quantization

    techniques to deep learning models can significantly reduce model size while maintaining accuracy. Significantly compresses the model size Minimal accuracy degradation
  25. What can be achieved with extremely low bit quantization technology

    The reduction in memory usage and computation by reducing the weight of the model leads to power savings and a smaller area of computational circuitry, which is key to achieving fast deep learning inference processing on constrained edge devices. Reduces memory usage Reduces amount of computation Power saving and smaller area of computational circuitry 25
  26. 26 Explanation video of extremely low bit quantization https://youtu.be/udliNii-FxI Extremely

    low bit quantization explanation video ー LeapMind's core technology supporting Efficiera ー Please check detailed explanation about extremely low bit quantization in following video.
  27. Ultra low power AI inference accelerator IP Efficiera 27

  28. 28 The New Standard for Edge AI

  29. Confidential: Internal Efficiera is an ultra-low power AI inference accelerator

    that can be implemented on an FPGA device or ASIC/ASSP device, and is specialized for CNN inference operations. Efficiera addresses various technical challenges including power consumption, cost, and heat dissipation, enabling the rapid introduction of on-device edge AI products to the market. 29
  30. 30 LeapMind's deep insight about deep learning Since the beginning

    of edge AI era, we have focused our hardware and software research efforts into model weight reduction, dedicated circuit design, and building the knowledge needed to deliver full-package solutions. Energy Efficient Features of Efficiera High Performance Small Footprint Scalable By minimizing the volume of data transmitted and the number of bits, the power required for convolution operations is reduced. By reducing the arithmetic logic complexity, the number of operation cycles is reduced and the arithmetic capacity per area/clock rate is improved. By minimizing the number of operated bits, the circuit area and SRAM size per arithmetic logic unit are minimized. Since the computing performance can be fine-tuned by adjusting the circuit configuration, it is possible to optimize the configuration and maximize the performance of Efficiera according to the task being performed.
  31. FPGA Benefits Efficiera Brings ASIC/ASSP It contributes to reducing the

    BoM cost of products with AI functions by integrating Efficiera on the same SoC FPGA device as the CPU and image input circuit Contributing to the reduction of device development costs by achieving practical arithmetic capacity without using advanced semiconductor manufacturing processes such as 5nm and 7nm The most suitable for adding AI capabilities to existing image processing FPGA designs It is able to build AI solutions by training custom data sets based on Efficiera-optimized pre-trained models Since RTL can be implemented using only standard cell libraries and memory, it can be used for many device designs. 31 Achieved 27.7 TOP/W of computing power in 12nm process *Measurement in development prototype
  32. Advantages with FPGA Devices BoM Cost Reduction AI functions can

    be added to existing circuits without additional component cost. LeapMind AI Accelerator IP “Efficiera®” Board DDR SDRAM SoC型FPGA DDR Ctlr CPU On-chip Memory Peripherals 32
  33. Efficiera's Application Areas 33 FPGA ASIC/ASSP Consumer Electronics Industrial

  34. 34 Efficiera product family Efficiera Trained model Optimized for Efficiera

    • Extremely low bit quantization AI inference accelerator IP • Optimized for FPGA implementation, also covers ASIC/ASSP • Deep Learning models optimized for Efficiera • Optimized for FPGA performance range • Trained for typical use cases • We also provide tools that enable customers to perform "fine tuning*". ※ A method to build a new model by reusing a part of an existing model
  35. Efficiera Use Case Higher Image Resolution 35 High-Quality Video Streaming

    Improved Quality of Captured Images Privacy Masking Deterioration Inspection Monitoring at Nursing Facilities Counting of Large Crowds Proximity Danger Detection
  36. We implemented Efficiera on EIZO's DuraVision EVS1VX, the visibility-enhancing system,

    and completed a joint performance evaluation for the construction of an edge AI system. Joint performance evaluation confirms Efficiera can deliver practical performance EIZO's visibility-enhancing system Board DDR SDRAM SoC FPGA DDR Ctlr CPU On-chip Memory Peripherals Efficiera® By taking advantage of the small footprint of Efficiera, one of its many features, it is possible to utilize the free space of the FPGA that is already installed in the DuraVision EVS1VX to achieve deep learning functions, without adding to or changing the hardware. It contributes to overall system cost reduction by achieving a single chip with FPGA 36
  37. Efficiera was so light that it can run deep learning

    in the free space of an existing FPGA without adding or changing hardware, and yet it can deliver practical performance. By combining conventional functions and deep learning on a single chip in FPGA, we were able to keep system costs low. We had frequent meetings to quickly share problems and progress, and the technical support was fast and thorough. What our customers have to say about Efficiera 37 Efficiera was able to achieve practical performance. It led to cost savings. The support was generous and quick.
  38. 38 60 FPS Object detection 60 FPS Pose estimation 60

    FPS Crowd counting 1–2 TOP/s 4–8 TOP/s 8–12 TOP/s 60 FPS Noise reduction 40–50 TOP/s As Efficiera can tune its computing efficiency by selecting the circuit configuration, it can cover not only image recognition tasks, such as object detection, but also the real-time processing of resolution improvement tasks that require a much higher performance range, such as super resolution. Efficiera performance scalability Hazard proximity Detection Watching over people in a nursing home Counting thousands of people in a flash Higher resolution for video footage
  39. 39 Knowledge gained from many projects & support system

  40. Knowledge gained from joint development

  41. Driving support technology 41 Classification of Non-defective / defective product

    Automatic control support for drones Automatic detection of scratches and cracks Danger detection by surveillance camera Foreign object detection Use cases Experience in joint development with partners in a wide range of industries
  42. 42 More than 150 projects and more LeapMind has joint

    research and development projects with many companies
  43. 43 JAXA:GIDLIE(Micro Smart Cameras for Space) Realization of the best

    selfie of the space plane Submit © JAXA Score the spacecraft selfies taken by the camera module with deep learning, and send back the only highest-scoring photos to Earth Project Cases
  44. 44 Maekawa Manufacturing: Meat Processing Robot Working on a joint

    development project for a cut positioning function using deep learning that will be included in a new model under development. Reduce misalignment of cut positions with deep learning Project Cases
  45. Powerful partner companies

  46. 46 We collaborate with partners to drive our business forward

    Aiming to realize practical and mass production of edge AI-equipped products with powerful partners
  47. 47 Efficiera FPGA Partner Program The Efficiera FPGA Partner Program

    was launched with the goal of co-creation of on-device AI products and solutions that solve customer issues. By participating in this program, partner will be able to combine your company's products and services with Efficiera in order to develop and provide package services and systems that meet your customer's needs. Realizing edge AI Achieving practical application and mass production of edge Al devices for our customers • Providing low-cost FPGA package solutions • Providing co-created solutions that incorporate IP cores from other companies • Joint promotion FPGA solution provider Partners
  48. 3.Members& Organization 48

  49. Uniqueness of LeapMind • 60% of members are engineers or

    researchers Tech venture • Members come from 12 countries • English and Japanese is used interchangeably, and meetings are often in English • Female employees play active roles at every layer and the positions of the organization including managers, researchers, and engineers Diversity • Flex system and flexible work style • Close relationship between board members and employees Freedom 49
  50. Confidential: Internal Engineer Researcher Business development Back office 50 Ratio

    by Job Type 60% of members are engineers or researchers
  51. Talents of LeapMind Researcher Consultant Deep Learning Engineers Hardware Engineers

    Machine Learning Engineers Application Engineers We have various engineers 51
  52. Confidential: Internal Office United State of America Japan England India

    Malaysia Mongolia Russia Taiwan China Thailand Vietnam Poland 52 Origin of Employees 12 Countries
  53. 53 4.Work style

  54. 54 How Our Engineers Work At LeapMind, our engineers are

    experts in their respective fields. While each team has a different development flow, a common thread throughout the company is the presence of the Design Doc. We also host regular events for engineers, such as Office Hour and Engineer MeetUp. Office Hour Employee-sponsored study sessions where they can learn about deep learning and other various topics with in-house experts Engineer MeetUp Event featuring talk sessions from CTO/VPoE, LT from engineers, Q&A and more Design Doc A document that describes what, why and how to make each project
  55. HackDays The biggest event at LeapMind is HackDays, which we

    take a break from our normal duties for long periods of time. These events allow you to hack a variety of things using your knowledge and the company's resources, whether it's developing something you love, trying something you've always wanted to do, or teaming up with different people to work on something different. The HackDays blog is also posted: https://leapmind.io/blog/2019/09/03/hackdays-2019-3q/
  56. 56 Business Interview We have posted business interviews on our

    website. Please check it. https://leapmind.io/careers/ Product Owner Interview General Manager Interview General Manager, Efficiera Division Katsutoshi Yamazaki Efficiera Product Owner, Efficiera Division Takuya Wakisaka I want to contribute changing people's lifestyle with Efficiera I want to contribute making society a place to enjoy diversity with Efficiera
  57. 57 Employee Interview We have posted employee interviews on our

    website. Please check it. https://leapmind.io/careers/ A good culture of encouraging commitment to following through a whole project. Researcher, Noise Reduction Team, Model Development Group, Efficiera Division Joel Nicholls Got inspired by courage of the team of LM to take on great visions. Engineer, Business Development Team, Commercialization Group, Efficiera Division Lily Tiong
  58. Confidential: Internal From time to time, we consider introducing a

    new system depending on phases! 1 on 1 We conduct individual meetings to encourage open communication and to support employee growth 58 Benefits and System At LeapMind, we are working on creating a system to support employees’ various work-styles Work Style Flexible work-styles such as flex system, remote work, and "refresh" leave Free Drinks Mineral water and coffee on the house Development Environment Support to improve the development environment, such as the supply of 4K monitors, laptops and high-back chairs Education Support Support for necessary expenses to improve skills, such as for taking Coursera courses and purchasing books Office Event Social gatherings and barbecues to share information and invigorate communication among all employees
  59. Confidential: Internal LeapMind is taking thorough countermeasures to prevent the

    spread of COVID-19 infections in Japan 59 Countermeasures for COVID-19 Work From Home Recommendations Over 80% of our members are working from home. We use Google meet and Slack for job communication and meetings Allowance for Work From Home In addition to the monthly work from home allowance, there is also a ”Remote Work Device Support” subsidy for equipment used in remote work Introduction of a Full-Flex System Full-flexible system with no core time, allowing you to work at your own pace Hosting In-House Events Remotely Organize online events, such as onboarding orientation, company wide meetings, HackDays and DL Office Hour (As of July 2021, subject to change depending on the situation of COVID-19) Interviews are basically conducted online only. Also, we are unable to project new recruiting from abroad for the time being, however, we have relocation support (Welcome Japan Package) to welcome new members from overseas. We would be very much appreciated if you could consider us again when we resume overseas recruitment!
  60. Confidential: Internal 60 Welcome Japan Package We provide a Welcome

    Japan Package to improve the employee experience to support those who are coming from abroad to join LeapMind Support in obtaining a work visa in Japan Partial expense covered for those moving from abroad Online interviews for those who live far away English support channel for personal inquiries Internal materials are written in both English and Japanese Full expenses covered for Japanese Language Proficiency Test JLPT (N1, 2) Aあ
  61. 61 Example of Machine Learning Engineer Interview Process Document Screening

    Coding Test & Engineer Interviews (2-3 times) Offer Hiring Committee All interviews and tests are conducted online.
  62. We have created an environment where everyone can work equally

    regardless of age, gender, nationality, or race. We, at LeapMind, want to work with someone who wants to make the most of their abilities and skills and also who can respect diversity. Our backgrounds aside, we all share a common goal. To create innovative devices with machine learning and make them available everywhere We are doing the most exciting things right now at LeapMind. Would you like to join us in achieving LeapMind’s vision?
  63. WE’RE HIRING https://leapmind.io/ https://leapmind.io/careers/ https://www.wantedly.com/companies/leapmind https://www.linkedin.com/company/leapmind-inc/ https://jobs.atcoder.jp/offers/list?f.CompanyScreenName=leapmind Official Page Careers

    Wantedly Linkedin AtCoder @leapmind_inc https://www.facebook.com/leapmind.io/