Vertex Perspectives | AI-optimized Chipsets | Part I
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The document discusses the increasing adoption of AI in various business applications, emphasizing the need for AI-optimized chipsets due to the high computing power required by deep learning technologies. It outlines the AI-driven ADAC loop, where applications generate data, enhancing algorithms and driving demand for advanced computing hardware. The analysis also highlights the role of AI in diverse sectors, from autonomous vehicles to agriculture, and foreshadows future shifts in performance focus and the emergence of novel AI chip technologies.
Vertex Perspectives | AI-optimized Chipsets | Part I
- 1. AI-Optimized Chipsets Mar 2018 Part I: Key Drivers
- 2. Source: When Moore’s Law Met AI by Azeem on Medium | icons8 Applications Data Algorithms Compute Businesses are increasingly adopting AI to create new applications to transform existing operations. These include connected devices, autonomous vehicles, on-device personal interfaces, voice interactions and AR. Applications Data AI Algorithms Computing Hardware Up to 30 billion more IoT devices are coming online by 2020, streaming data that helps build smarter objects, homes, inform consumer lifestyle, enhance security and energy management. Most breakthrough approaches in deep learning use significant computing power. A neural net might have dozens of connected layers and billions of parameters, requiring a step-wise increase in level of computing power. This positive, recursive ADAC loop where new applications generate more data, in turn enhancing algorithmic complexity, driving demand for higher computing performance. 1 2 3 4 Businesses are increasingly adopting AI to create new applications, driving the development of AI-optimized chips The ADAC (Applications – Data – Algorithms – Computing Hardware) Loop
- 3. Software SoftwareHardware Autonomous Driving Speech Recognition & NLP Computer Vision Sensors Business Intelligence AI Platform Data Path Planning AI-Optimized Chipsets Industrial Applications Robotics Computing • To date, deep learning technology has primarily been a software play. • Existing processors were not originally designed for these new applications. • Hence the need to develop AI- optimized hardware. These new applications are built on other technology and infrastructure layer solutions Source: Vertex | AutomotiveIQ | Icon 8 | Taranis | Kryon Systems | Horizon Robotics • Sensing uses advanced computer vision and perception. • Visual tasks including lane detection, pedestrian detection, road signs recognition and blind-spot monitoring are handled more effectively with deep learning. Path planning: Simple machine learning algorithms are sufficient to handle driving in high resolution mapped cities or along fixed routes. Deep learning is more suitable in complex situations, (e.g. multiple unknown destinations or changing routes). Taranis offers a comprehensive and affordable crop management solution, and the pest and disease prediction algorithms using deep learning to continually improve accuracy. Kryon Systems delivers innovative, intelligent Robotic Process Automation (RPA) solutions using patented visual and deep learning technologies. Horizon Robotics is the leader of embedded AI with leading technologies in autonomous driving perception and decision-making, deep learning algorithms and AI processor architecture. Examples of Vertex Portfolio Companies that employ deep learning in their solutions Hardware ApplicationTechnologyInfrastructure
- 4. Edge Resident Hybrid Solutions Cloud Hosted Consumer | Retail • Gaming • Smart Displays • Personal Assistants • Ad Targeting & E-Commerce Transportation • Autonomous Vehicles • Transportation & Grid Control • Traffic & Network Analytics Enterprise • Delivery Drone • Warehouse Robots • Cyber Security • Sales, Marketing & Customer Services Commodities • Field Drones & Robots • Climate, Water • Energy & Flow Control • Field Sensor Data Analytics Industrial | Military • Cobots • Unmanned Systems • Factory Control & Surveillance • Factory & Operations Analytics Healthcare • Medical Imaging • Surgical Robots • Medical Diagnostics • Clinical Analytics That may reside in the cloud, on edge devices or in a hybrid environment Source: : Moor Insight & Strategy
- 5. Autonomous Vehicles • In an autonomous car, cameras will generate between 20–60 MB/s, radar upwards of 10 KB/s, sonar 10–100 KB/s, GPS will run at 50 KB/s, and LIDAR will range between 10–70 MB/s. • Each autonomous vehicle will be generating approximately 8GB/s, 4TB per day. • Autonomous vehicles require a reliable solution with an ultra-low latency of 1ms. Agriculture • Descartes Labs uses deep learning to process satellite imagery for agricultural forecasts. • It processes over 5TB of new data every day and references a library of 3PB of archival satellite images. • By using real time satellite imagery and weather models, Descartes Labs provides highly accurate weekly forecasts of US corn production compared to monthly forecasts provided by the US Department of Agriculture. Source: NovAtel Source: Descartes Labs And all point to significantly higher data generation Source: : Intel | IEEE Spectrum, | Deep Learning: An Artificial Intelligence Revolution by Ark Invest | Descartes Labs | Reducing 5G Latency Benefits Automotive Safety by Bill McKinley | NovAtel
- 6. 500-1000 ms 200 ms 100 ms 1 ms 100KB/s 384KB/s-2MB/s 150KB/s-450MB/s 10 GB/s 2G GSM | GPRS EDGE | CDMA 1990- 2000 3G UMTS CDMA 2000 2000-2010 4G LTE LTE-A 2010-2020 5G >2020 Source: Wi360 50B Number of IoT devices by 2020 Top IOT Applications Smart Home Wearables Connected Industries Connected Car Smart City Smart Energy The 5G Evolution: Latency for Different Generations of Cellular Networks Coupled with the growth of IoT and 5G networks, a data deluge of high volume, velocity and variety is expected IoT and Exponential Growth in Devices The growth of IoT and 5G networks expected to generate a data deluge of high volume, velocity and variety Source: Gartner Volume Velocity Variety Source: : IoT Analytics | Intel | IEEE Spectrum, | Deep Learning: An Artificial Intelligence Revolution by Ark Invest | Wi360 | icon8 Source: World Economic Forum
- 7. • Compounding the power of deep learning, the neural nets themselves have become larger and more sophisticated, as measured by their number of free “parameters”. • Parameters are dials used to tune the network’s performance. Generally, more parameters allow a network to express more states and capture more data. • It endows computers with previously unimaginable capabilities - understanding photos, translating language, predicting crop yields, diagnosing diseases etc. Enabling AI to write software to automate business processes that humans are unable to write. Source: Andrew Ng, Ark Invest Unlike other machine learning algorithms, those associated with deep learning scale with increasing training data “The process could be very complicated…As a result of this observation, the AI software writes an AI software to automate that business process. Because we won’t be able to do it. It’s too complicated... For the next couple of decades, the greatest contribution of A.I. is writing software that humans simply can’t write. Solving the unsolvable problems.” Jensen Huang CEO | NVIDIA Source: Inside Microsoft's FPGA-Based Configurable Cloud by CTO Mark Russinovich | Nvidia | Deep Learning: An Artificial Intelligence Revolution by Ark Invest | Fortune
- 8. Deep Learning vs. Other Programming Techniques Given future process complexities, AI will be needed to automate the programming process by coding dynamically Source: Ark Invest Management LLC, Yoshua Bengio OutputInput Output … Input Data Trained Program Input Output Hand Crafted Program 1980s Classic Programming • Software developer codes the solution in software, which then gets executed in a deterministic and obtuse fashion. • This works for simple, well-defined problems but breaks down for more complex tasks. 2000s Machine Learning • Improves upon classic programming by replacing some stages of the program with stages that can be trained automatically with data • Enabling computers to perform more complex tasks (e.g. image and voice recognition). • The software developer focuses less on coding, more on building models which require enormous datasets to recommend a best output. 2010s Deep Learning Entire program is replaced with stages that can be trained with data • Programs can be far more capable and accurate. • Requires less human effort to create. Source: Deep Learning: An Artificial Intelligence Revolution by Ark Invest | icon8
- 9. Source: Morningstar | Vertex | icon8 But existing processors were not originally designed for new AI applications. Hence the need to develop AI-optimized hardware Strengths Limitations Training Rank Inference Rank Leading Vendors • General-purpose, in servers and PCs • Sufficient for inference • Serial-processing is less efficient than parallel-processing • Highly parallel, high performance • Uses popular AI framework (CUDA) • Less efficient than FPGAs • Scalability • Inefficient unless fully utilised • Reconfigurable • Good for constantly evolving workloads • Efficient • Difficult to program, • Lower performance versus GPUs • No major AI framework • Best performance, • Most energy and cost efficient • Fully customizable • Long development cycle • Requires high volume to be practical • Quickly outdated, inflexible 2 2 13 1 3 N.A. N.A.
- 10. Looking ahead This is the end of Part I of a 4-part series of Vertex Perspectives that seeks to understand key factors driving innovation for AI-optimized chipsets, their industry landscape and development trajectory. In Part II, we review the shift in performance focus of computing from general application to neural nets and how this is driving demand for high performance computing. To this end, some startups are adopting alternative, novel approaches and this is expected to pave the way for other AI-optimized chipsets. In Part III, we assess the dominance of tech giants in the cloud, coupled with disruptive startups adopting cloud-first or edge-first approaches to AI-optimized chips. Most industry players are expected to focus on the cloud, with ASIC startups featuring prominently in the cloud and at the edge. Finally in Part IV, we look at other emerging technologies including neuromorphic chips and quantum computing systems, to explore their promise as alternative AI-optimized chipsets. We are most grateful to Emmanuel Timor (General Partner, Vertex Ventures Israel) and Sandeep Bhadra (Partner, Vertex Ventures US) for their insightful comments on this publication. Do let us know if you would like to subscribe to future Vertex Perspectives. Source: Vertex
- 11. Disclaimer This presentation has been compiled for informational purposes only. It does not constitute a recommendation to any party. The presentation relies on data and insights from a wide range of sources including public and private companies, market research firms, government agencies and industry professionals. We cite specific sources where information is public. The presentation is also informed by non-public information and insights. Information provided by third parties may not have been independently verified. Vertex Holdings believes such information to be reliable and adequately comprehensive but does not represent that such information is in all respects accurate or complete. Vertex Holdings shall not be held liable for any information provided. Any information or opinions provided in this report are as of the date of the report and Vertex Holdings is under no obligation to update the information or communicate that any updates have been made. About Vertex Ventures Vertex Ventures is a global network of operator-investors who manage portfolios in the U.S., China, Israel, India and Southeast Asia. Vertex teams combine firsthand experience in transformational technologies; on-the-ground knowledge in the world’s major innovation centers; and global context, connections and customers. Authors Yanai ORON General Partner Vertex Ventures Israel yanai@vertexventures.com XIA Zhi Jin Partner Vertex Ventures China xiazj@vertexventures.com Brian TOH Director Vertex Holdings btoh@vertexholdings.com Tracy JIN Director Vertex Holdings tjin@vertexholdings.com ZHAO Yu Jie Associate Investment Director Vertex Ventures China zhaoyj@vertexventures.com
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