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Neuromorphic Chipsets - Industry Adoption Analysis



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The concept of emulating neurons on a chip could enhance complex operations to make business decisions secure and cost-effective. Parallel connected neurons can boost AI verticals compared with the conventional processing systems. Non-stop learning and pattern recognition using this human brain architecture can help compute signals and data in the form of visual, speech, olfactory, etc., to perform real-time operations as well as predict outcomes based on detected patterns. Neuromorphic chipsets can also enhance performance owing to their low-power consumption to process AI algorithms.

Based on patent data, this report analyzes the ongoing R&D and investments in neuromorphic chipsets by major institutions across the globe to reveal the top innovators and technology leaders in this space.

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Neuromorphic Chipsets - Industry Adoption Analysis

  1. 1. Proprietary and Confidential, Copyright © 2019, Netscribes, Inc. All Rights Reserved The content of this document is confidential and meant for the review of the recipient. Disclaimer: The names or logos of other companies and products mentioned herein are the trademarks of their respective owners SAMPLE REPORT The Next Revolution in AI Hardware May 2019 Neuromorphic Chipsets Applications, Competitive Intelligence, and Patents Analysis
  2. 2. SAMPLE REPORT Introduction ▪ Introduction to the Architecture and Properties ▪ Implementation of Neural Algorithms on Hardware ▪ Spiking Neural Networks Driving Neuromorphic Computing ▪ Neuromorphic Chipsets Transforming the AI Landscape ▪ Key Features Enabling Industry-wide Adoption Industry Adoption of Neuromorphic Chipsets ▪ Automotive ▪ IoT Ecosystem ▪ Financial Services ▪ Cybersecurity ▪ Space Industry ▪ Medical Systems Competitive Intelligence ▪ Key Players ▪ Active Research Projects ▪ Increased Interest in Emerging Entities ▪ Overview of the Startup Ecosystem R&D In the Neuromorphic Hardware Domain ▪ Universities active in the Domain Patent Trend Analysis ▪ Overview of the Patenting Activities ▪ Top Technology Domains ▪ Focus Areas of the Technology Domains ▪ Assignees across Geographies ▪ Overview of Assignees ▪ Top Assignees ▪ Emerging Players and Top Universities ▪ Other Important Activities Key Takeaways Appendix ▪ Definitions ▪ References ▪ About ROGM Table of Content
  3. 3. Click to edit Master title style NEUROMORPHIC CHIPSETS 3 SAMPLE REPORT Executive Summary (1/3) Technology • Neuromorphic chipsets have integrated processing and storage blocks, which eliminates the bottlenecks related to speed, power consumption and efficiency that are present in the traditional Von Neumann architecture • Neuromorphic chipsets have the potential of transforming the AI chipset market with near term and long term use cases across various industries • Neuromorphic chipsets are laying the foundation to achieve artificial general intelligence (AGI) capabilities Industry Adoption • Neuromorphic chipsets are still available for prototyping and experimenting, with the initial architectures gradually finding applications in various industries • ………….. IoT ….. are near term application of neuromorphic chipsets • Massive …….. and inherent …… properties make the neuromorphic architectures suitable for stock predictions and self-driving scenarios • Long-term applications in ….. and space industries
  4. 4. Click to edit Master title style NEUROMORPHIC CHIPSETS 4 SAMPLE REPORT Executive Summary (2/3) Frontrunners • ……….is a highly accessible chipset in the market for neuromorphic demonstrations and closest to being fully operational • …………. Neuromorphic Research Community is attracting many academic, government and corporate research groups to develop hands-on, extended neuromorphic solutions • ……………. is an emerging entity directly competing with Qualcomm, Intel, and IBM Future Innovators • Several startups are focusing on …………… neuromorphic hardware for edge operations • …………… and …………. are working on always-on- voice functionalities • Prophesee has a future focus towards human augmentations • …………. Lab, ………….., and ……. are focusing on neuromorphic vision • ……………. is exploring AGI models for robots
  5. 5. Click to edit Master title style NEUROMORPHIC CHIPSETS 5 SAMPLE REPORT Executive Summary (3/3) Patent Trends • Top patent assignees including IBM, Qualcomm, …………., ………….., ……….. account for about half of the total patent count • Patent publications are related to memory, memristors, stochastic operations, neurosynaptic cores, plasticity, and many other technology areas • Another Brain, …………, …………., Kneron, and ………… are emerging players Research Universities • Researchers from ………….. University have investigated architectures like ………… and Braindrop. These researchers are also working on a stealth-mode startup, …………….. • The University of ………….. is targeting real time processing of event-based sensory signals • …………… is actively collaborating with …………… universities for developing 17 core neuromorphic technologies
  6. 6. SAMPLE REPORT Introduction to the Architecture and Properties
  7. 7. NEUROMORPHIC CHIPSETS 7 SAMPLE REPORT Neuromorphic Chipsets: An Overview of the Technology The third generation of neural networks is closer to biological neurons and is being investigated to develop a true neuromorphic architecture. The future implementations of neuromorphic designs will lead to autonomous self-learning systems and high performance architectures that can process quintillion calculations per second. To keep pace with AI software development, considerable efforts are being made to build hardware that can process AI algorithms. It seems that the future of AI hardware will be defined by biologically-inspired neuromorphic chipsets, which will provide a real time boost to AI systems. Brain-like chips will help showcase natural intelligence in major AI applications in the long term. These chips include desirable characteristics for intelligent sensors, and the ultimate aim is to develop process technologies, materials, memories, and other building blocks for the integration of neuron chips into sensors. The major features that are driving adoption of neuromorphic chipsets include low power consumption, stochastic operations, pattern recognition, faster computation, fault tolerance, and scalability. The concept of emulating neurons on a chip could enhance operations and make business decisions reliable and cost-effective. Parallel connected neurons can boost AI verticals significantly compared with the conventional processing systems. Non-stop learning and pattern recognition leveraging the human brain architecture design can help compute signals and data in the form of visual, speech, and olfactory inputs to perform real time operations and predict outcomes based on detected patterns.
  8. 8. NEUROMORPHIC CHIPSETS 8 SAMPLE REPORT Neuromorphic Chipsets: Introduction to the Concept and Architecture Data from various sensory organs of a human body automatically generates spikes between synapses – that are sent to neurons, which in turn enables the brain to make decisions Biological Brain Neuromorphic Chips Data requires transformation into spikes between synapses that can be used to train neural networks and inference engines, and ultimately perform various application-specific tasks The neuromorphic chips exhibit human brain-like capabilities for achieving high connectivity, parallelism, and real time operation by collocating memory and processing Synapses Synapses receive signals from other neurons as voltage spikes Biological Neurons Function in a smooth analog pattern of voltage Axons Transmit voltage spikes to other neurons Connections Emulated synapses that communicate by means of spikes Emulated Neurons Integrate incoming signals for parallelism Wires Wires are emulated axons that mimic an axon’s operations Neuromorphic chips are obtained by emulating the brain’s neural activities with the help of artificial synapses, neurons, and axons – recreated on a chip Inspiredby BiologicalBrain EmulationofBrain Activities
  9. 9. NEUROMORPHIC CHIPSETS 9 SAMPLE REPORT How is Neuromorphic Architecture Different? Von Neumann Architecture Neuromorphic Architecture Neuromorphic architectures address challenges like high power consumption, low speed, and other efficiency-related bottlenecks prevalent in the traditional von Neumann architecture Architecture Bottleneck CPU Memory Neuromorphic architectures integrate processing and storage, getting rid of the bus bottleneck connecting the CPU and memory Encoding Scheme and Signals Unlike the von Neumann architecture with sudden highs and lows in the form of binary encoding, neuromorphic chips offer a continuous analog transition in the form of spiking signals Devices and Components CPU, memory, logic gates, etc. Artificial neurons and synapses Neuromorphic devices and components are more complex than logic gates Versus Versus Versus
  10. 10. NEUROMORPHIC CHIPSETS 10 SAMPLE REPORT Neuromorphic Chipsets vs. GPUs Parameters Neuromorphic Chips GPU Chips Basic Operation Based on the emulation of the biological nature of neurons onto a chip Use parallel processing to perform mathematical operations Parallelism Inherent parallelism enabled by neurons and synapses Require the development of architectures for parallel processing to handle multiple tasks simultaneously Data Processing High High Power Low Power-intensive Accuracy Low High Industry Adoption Still in the experimental stage More accessible Software New tools and methodologies need to be developed for programming neuromorphic hardware Easier to program than neuromorphic silicons Memory Integrated memory and neural processing Use of an external memory Limitations • Not suitable for precise calculations and programming- related challenges • Creation of neuromorphic devices is difficult due to the complexity of interconnections • Thread limited • Suboptimal for massively parallel structures Neuromorphic chipsets are at an early stage of development, and would take approximately 20 years to be at the same level as GPUs. The asynchronous operation of neuromorphic chips makes them more efficient than other processing units.
  11. 11. NEUROMORPHIC CHIPSETS 11 SAMPLE REPORT Neuromorphic Chipsets: Where Does it Fall in the Timeline? OverallSystemPerformance 20XX 20XX2017 FPGA 2.0 eMRAM Carbon Nanotube 1-nm Transistor Neuromorphic Computing Quantum Computing Technology nodes are progressively reducing in size, while boosting the overall performance of the systems. However, Moore’s Law is reaching its limit, and once 1-nm transistors are successfully implemented, reducing the size of elements further without compromising on the performance side of the ratio would be a major hurdle. Therefore, neuromorphic computing is likely to be the next level of evolution in processing architectures.
  12. 12. NEUROMORPHIC CHIPSETS 12 SAMPLE REPORT Spiking Neural Networks Driving Neuromorphic Computing Artificial Intelligence Machine Learning Neural Network Artificial Neural Network 1st Gen Convolutional Neural Network Spiking Neural Network 2nd Gen 3rd Gen ANN and CNN architectures are characterized by a single, continuous- valued activation Event-driven, functionally similar to biological neurons, and biologically more realistic than ANN Neuromorphic computing provides multiple approaches for developing AI technologies of tomorrow that are deriving intelligence from real-world scenarios with a massive amount of data. Different generations of neural networks models have been devised to develop and understand neuromorphic capabilities to achieve brain-like efficiency Neural Network Advancements
  13. 13. NEUROMORPHIC CHIPSETS 13 SAMPLE REPORT Neuromorphic Chipsets Transforming the AI Landscape Exascale ComputingSelf-learning Intelligent Systems Neuromorphic is among one of the high-performance architectures critical for the enablement of exa- FLOPS (1018 FLOPS) computing 2040 >2050>2020 Neuromorphic designing and fabrication targeted towards developing self-optimizing and self- configuring AI systems for different industries Neuromorphic AI is a radical path towards ensuring the exponential growth of machine intelligence and human augmentation Artificial General Intelligence Artificial Super Intelligence Surpassing Human Capabilities
  14. 14. NEUROMORPHIC CHIPSETS 14 SAMPLE REPORT Neuromorphic Chipsets: Key Features Enabling Industry-wide Adoption Low Power Consumption The human brain performs complex computations on a small power budget of about 20 W of power, compared with the supercomputers that require kW or MW power for AI applications. Fault-Tolerant Neuromorphic chips continue to operate even after the failure of a few components of the chip. This reduces the cost of production of the chips due to lower fabrication tolerances. Stochastic Operation Current AI chipsets are ordered, and operate in a calculated manner. However, neuromorphic chips are stochastic in nature, because of which they can be used for all applications. Pattern Recognition The low power pattern recognition of neuromorphic hardware helps to classify objects, make predictions, or anticipate conditions. It is also possible to understand the context of such patterns by using neuromorphic chips. Faster Computation The inherent massive parallelism and low latency factor of neuromorphic chips make it possible to perform complex computations faster. Neuromorphic chips use less training data compared with other intelligent chipsets. Scalability Neuromorphic architectures can be employed in edge applications and can also be scaled up for server applications. Advantages Leading to Ground-Breaking Use Cases
  15. 15. SAMPLE REPORT Industry Adoption of Neuromorphic Chipsets
  16. 16. NEUROMORPHIC CHIPSETS 16 SAMPLE REPORT Neuromorphic Chipsets: Potential to Disrupt Industries IoT Ecosystem Neuromorphic chipsets are appearing first at the edge segment of the IoT ecosystem where there is a need for ………………………………….for various applications. Adoption Impact Use Case Automotive Neuromorphic chipsets are suitable for classification tasks in ………….and, in the short term, can be integrated in conventional hardware that deals with ………… navigations for traffic. Adoption Impact Use Case Space Industry Neuromorphic chipsets for the space industry are ……….…………..and are expected to be commercialized after non- space applications. Adoption Impact Use Case Financial Services and Cybersecurity Noteworthy features such as parallel operations, autonomous learning, and prediction of patterns are expected to drive the adoption of neuromorphic chipsets in financial services and cybersecurity. Adoption Impact Use Case Neuromorphic chipsets could provide high performance medical systems owing to their ability to make predictions related to critical medical analysis based on pattern recognition. Medical Systems Adoption Impact Use Case MediumHigh Low Near Term Middle Term Long Term
  17. 17. NEUROMORPHIC CHIPSETS 17 SAMPLE REPORT Neuromorphic Chipsets: Automotive Industry Pattern Recognition Neuromorphic chips are ideal for classification tasks and can be used for different scenarios in autonomous driving Advantages of Neuromorphic Chips in Automotive Industry Faster Computation Spiking neural networks has the inherent advantage of faster recognition speed Stochastic Operation Brain chips are efficient in a noisy environment, e.g., self- driving vehicles compared with static deep learning solutions Low Power Consumption Neuromorphic chips have higher power efficiency compared with existing solutions Current Scenario of AI Chips Future AI Chip ExpectationsBridging the Gap with Neuromorphic Chips Current architectures are good at precise calculations and assure rule-based driving characteristics Autonomous driving market requires constant improvement in AI algorithms for high throughput with low power requirement To increase the performance of object detection, higher model complexity is followed Key requirement is to ensure that neuromorphic computing with the brain-inspired chip is compatible with established architectures Future chips for autonomous connectivity need to be cost-effective, as AI chips account for a major portion of the cost of self-driving vehicles As the life cycle of a chip is shorter than the life cycle of a vehicle, frequent software and hardware updates will be required during one automobile life cycle to ensure high performance and safety in self-driving vehicles and to position the IC manufacturers in the value chain A viable solution for the future of level 4-5 self-driving vehicles would be a combination of neuromorphic chips with the Von Neumann architectures, such that the neuromorphic chip can be used for object detection and classification tasks, while the Von Neumann architecture is used for precise calculations to ensure correct rule-based driving behavior.
  18. 18. NEUROMORPHIC CHIPSETS 18 SAMPLE REPORT Neuromorphic Chipsets: IoT Ecosystem Advantages of Neuromorphic Chips in IoT Ecosystem Current Scenario of AI Chips Future AI Chip ExpectationsBridging the Gap with Neuromorphic Chips The IoT ecosystem is moving toward the edge. Currently, edge devices rely on the cloud for computation Custom, application-specific SoCs are available for meeting the low-power requirements of IoT devices Wireless interoperability is needed to reduce fragmentation in the IoT ecosystem Design and verification requirements are the challenging parameters AI chips will support absolute edge computing that does not require the internet to perform operations Chip architectures would be general purpose for meeting ultra-low-power requirements with enhanced performance and speed Future chips will have autonomous learning capabilities to cater to different industries Optimized neuromorphic hardware can largely ………………… …………………… ………………………architectures is building up and neuromorphic chips perfectly fit the bill. Pattern Recognition Neuromorphic chips can efficiently process voice, image, and signal data involved in various IoT user interfaces and sensors Scalability Neuromorphic chips are scalable to the server level that would benefit IoT scenarios that require hybrid architectures Low Power Consumption Innovation at the edge requires low-power and energy- harvesting devices Faster Computation Real time learning capabilities will be essential for various mobility applications in the IoT ecosystem
  19. 19. NEUROMORPHIC CHIPSETS 19 SAMPLE REPORT Neuromorphic Chipsets: Financial Services Advantages of Neuromorphic Chips in Financial Services Current Scenario of AI Chips Future AI Chip ExpectationsBridging the Gap with Neuromorphic Chips AI chips require huge training data for mathematical calculations, which delays the overall operation Cost and power consumption are the problems when it comes to application scaling Hyperparameter tuning is the current challenge for machine-learning algorithms AI solutions with real time processing will be required to avoid losses incurred due to delayed outputs Optimized power consumption is expected without much emphasis on reducing the form factor Focus on designing algorithms that can adaptively choose and optimize models in response to the information observed Neuromorphic chips will drive the mission-critical situations in the stock market. …………. ………………….. , therefore eliminating delays. Pattern Recognition ………. Low Power Consumption Neuron chips are suitable option for predicting unconventional and high frequency trading patterns Faster Computation ……………
  20. 20. NEUROMORPHIC CHIPSETS 20 SAMPLE REPORT Neuromorphic Chipsets: Cybersecurity Advantages of Neuromorphic Chips in Cybersecurity Current Scenario of AI Chips Future AI Chip ExpectationsBridging the Gap with Neuromorphic Chips Current solutions sequentially match small chunks of data against a library of suspicious patterns The nature of current cybersecurity protocols follow a proactive and counter-response approach AI chips will identify patterns in encrypted packets that could point to malicious or unusual payloads inside the traffic The aim is to work toward predictive cybersecurity postures Training and inference systems for the detection of a wide range of random anomalous behaviors of computers and networking systems is required The inherent parallel processing model of …………………. ………………………………. and predictive alerts associated with ……………….. potential threats and attacks. Pattern Recognition SNNs can learn on the fly, which is an advantage in detecting new attack behaviors or vectors Low Power Consumption Faster Computation Best suited to anomaly detection in data mining procedures and predict potential threats
  21. 21. SAMPLE REPORT Competitive Intelligence
  22. 22. NEUROMORPHIC CHIPSETS 22 SAMPLE REPORT Competitive Intelligence: An Overview of the Industry Players Several public and private entities are committed towards building brain-inspired hardware for the future of AI. The major players active in the neuromorphic chipset domain include IBM, Qualcomm, Intel, Brainchip, Samsung, HP, HRL laboratories, and General Vision. Collaborative efforts are also being made to apply neuromorphic research to real-world applications. Samsung is working in partnership with universities to develop core neuromorphic technologies. Research projects by DARPA and European Union started years ago, and are continuously evolving to leverage neurosciences for high performance architecture levels. Significant increase in the number of startups and investments in the domain showcase the level of interest in the neuromorphic market. These startups are providing differentiated offerings and are setting new frontiers in the deep learning architectures. While most of these entities are focusing on edge operations, always-on applications and neuromorphic vision systems, few of them are already pursuing long- term goals. § Vicarious is targeting combination of probabilistic models with cognitive science for AGI, with a future aim of incorporating common sense in robots § Prophesee plans to use neuromorphic vision system for building medical devices to restore vision to the blind A step towards the development of neuromorphic hardware could be the availability of design kits for fabless groups, universities, and industry players.
  23. 23. NEUROMORPHIC CHIPSETS 23 SAMPLE REPORT Neuromorphic Chipsets: Key Players Company Name/Project Implementation Market Readiness Application Focus Plans Ahead ….. ……..: Neurosynaptic processor containing 5.4 billion transistors 28-nm, low-power CMOS process, reduced neuron switching by 99% on average of typical network Accessible for prototyping and demonstrations Robotics, medical, automotive, gesture recognition, machine learning, mobile, etc. Aims to lower AI-training power and release APIs to its ecosystem partners to hook up real time sensors …….. ……….: Neural processing unit that is reprogrammable and supports parallelism CMOS process, based around NPU, AI Accelerator Chip And software API Prototype demonstration Robotics, image recognition, big data processing Collaborations for testing the technology Intel Loihi: Asynchronous neuromorphic core with 130,000 neurons and 130 million synapses 14-nm FinFET process, functional over 0.5V - 1.25V Prototype AI edge, image recognition, robotics, etc. Plans to include >100 billion synapses in Loihi system by 2019 and solve LASSO optimization problems ………. …………: System-on-chip with 1.2 million neurons and 10 billion synapses CMOS process Expected to be in market in 2019 Edge applications, fintech, automotive, cybersecurity, surveillance and machine vision Collaborating with global manufacturers for early adoption of Akida Other Entities Other entities are also exploring neuromorphic chipsets including Neuromem (a General Vision Company), Numenta, HP and Samsung Corporates such as IBM, Intel, and Qualcomm are actively working on projects on next-generation AI chipsets. The most fully developed neuromorphic platform is …….. ……..
  24. 24. NEUROMORPHIC CHIPSETS 24 SAMPLE REPORT Neuromorphic Chipsets: Increased Interest in Emerging Entities
  25. 25. NEUROMORPHIC CHIPSETS 25 SAMPLE REPORT Neuromorphic Chipsets: Overview of the Startup Ecosystem - (1/5) …. develops ultra-low-power and fully programmable neuromorphic computing technology for sensor analytics and machine learning applications. This brain-inspired silicon intelligence is targeted towards smart homes, automotive, and healthcare. USD 15 Mn The company is aiming to support AI use cases and functions particularly for …………. Some of the applications include autonomous navigation platforms for cars, robots, and drones. Additionally, GrAI plans to advance towards market deployment. ………. is a provider of neuromorphic …………………………integrated circuit technology. It has developed a proprietary analog, ultra-low- power, always-on sensing chip. The solutions will help to overcome the challenges associated with data and power. USD 3.5 Mn The company plans to use the funds received for building a team and delivering its first product. Furthermore, it is ……………….. Fund suggesting a potential way forward to bring ubiquitous voice-first experiences in low- power and portable devices. ……… is a provider of mixed-signal neuromorphic processors that are based on a ………………………... These processors are based on reconfigurable real-time neural networks that enable ultra-low-power and ultra-low- latency AI edge computing applications. USD 2.7 Mn The company aims to build end to end dedicated neuromorphic IP blocks, ASICs, and SoCs as full- custom computing solutions that integrate neuromorphic sensors and processors. It is also planning to push real time ECG recording to smartwatch-makers. Technology Overview Future FocusCompanies HQ: Paris, France Founded: 2016 HQ: West Virginia, US Founded: 2015 HQ: Zurich, Switzerland Founded: 2017
  26. 26. SAMPLE REPORT R&D in the Neuromorphic Hardware Domain
  27. 27. NEUROMORPHIC CHIPSETS 27 SAMPLE REPORT Neuromorphic Chipsets: Universities Active in the Domain - (1/2) Developed Neurogrid and Braindrop architectures based on encode- transform-decode mechanism. These architectures are designed using neural engineering frameworks used to synthesize neuromorphic networks. Jointly developing a neuromorphic chipset to realize AI functions such as deep learning and data processing capabilities, including image and sound. The project is aimed at developing 17 core technologies in the neuromorphic market. Jointly developing reconfigurable spike- routing architectures for on-chip local learning in neuromorphic systems based on multiple routing schemes. In addition, pointer-based schemes are applied to global routing for multicore neuromorphic clusters which may support millions of neurons and synapses. Developed a hierarchical and mesh-based routing methodology for minimizing both memory requirements and latency, while maximizing programming flexibility to support event-based neural network architectures. In the future, this method can be used in real time processing of event-based sensory signals. Developed the Darwin processing unit, fabricated by CMOS technology, targetting resource-constrained, low power small embedded devices. Jointly developing an automated mapping and efficient debugging framework for implementing DNN onto a neuromorphic chip with crossbar array of synapses. Developed a neuromorphic chip that supports online pattern recognition. It works on STDP mechanism and teacher signals supporting both supervised and unsupervised learning. Developed a full-scale neuromorphic chip simulator that integrates both RRAM model and NoC model. The neurosynaptic core architecture contains an RRAM crossbar array for storing synaptic weights and performs weight accumulation through a current sum in analog domain.
  28. 28. SAMPLE REPORT Patent Analysis of Neuromorphic Chipsets
  29. 29. NEUROMORPHIC CHIPSETS 29 SAMPLE REPORT Patent Trend Analysis: An Overview of the Patenting Activities The neuromorphic hardware technology involves more than 190 applicants. IBM has the highest patent filings (162 unique filings), followed by Qualcomm (85 unique patent filings). The other key IP holders are SK Hynix and Intel. With growing interest, new players such as Brainchip are also emerging with low-power neuromorphic voice activation systems and autonomous feature extraction using SNN. Of the top 20 assignees, 10 assignees are based in US and six are from South Korea. Several key IP collaborations have been identified between universities and key entities. Start-ups such as Another Brain, Nantero, Neuramatix, Kneron, and Knowm are also filing patent publications addressing key requirements of neuromorphic chipsets. Despite neuromorphic chipsets being at an early stage of development, the patent filing activity is gaining interest across key semiconductor companies, R&D centers and universities. The development of autonomous systems is a prominent focus area in the overall patent study. Other notable area of interest has been investigation of neuromorphic systems or electronic synapses for the implementation of reinforcement learning. Several patent publications are focusing on factors such as interconnect, fabrication techniques, material, memory integration and edge devices. The patent landscape documents how innovation in neuromorphic chipset is increasing globally and new insights related to the filing and technology trend.
  30. 30. NEUROMORPHIC CHIPSETS 30 SAMPLE REPORT Patent Trend Analysis Analysis of patent data from 1999 to 2018 provides several insights into the neuromorphic hardware landscape: q 700 patent publications were identified specific to neuromorphic hardware q There are few patent filings in the first 10 years (1999- 2008), as the concept of neuromorphic engineering was only developed in the 1980s q Filing spike started in 2012, when more than 20 patents were filed by IBM q Trends in patent filings during 2013–2015 have been stagnant with a very slight increase in 2015 q The patent filing trends show that maximum filing activity was in 2016 The patent study has been conducted to understand the evolution of patent publications and countries of patent filings. The key assignees and technology domains shaping the neuromorphic IP landscape have also been covered to assess upcoming trends. No.ofPublications Priority Year *The timeline considered for the study is 1999-2018
  31. 31. NEUROMORPHIC CHIPSETS 31 SAMPLE REPORT Patent Trend Analysis: Top Technology Domains Neuron-powered chips will become the enabler of many future applications. Some of the major aspects identified from the patent study are systems/chips or apparatus focusing on brain models, spike generation, neuro-synaptic cores, etc., to facilitate large-scale neural hardware implementation. Circuits (136) Low power neural models or circuits with high integration and small chip area Neurons (197) Architectures that support parallel processing, stochastic operations, and back propagation implementation Edge Devices (50) Autonomous systems, olfactory auditory pattern classification, and image recognition Fabrication (52) Synapse array with efficiency, scalability, performance, and manufacturability Synapse (90) Integration for analog computation, dynamic processing and reinforcement learning Memory (48) Memory unit for synapse or stochastic environment to suit neuromorphic applications Memristors (36) Memristive neuron circuits for signal processing, pattern recognition, and control systems Interconnects (12) Schemes for interconnections in reconfigurable/peripheral units in neuron hardware Others (79) Chip communication, back propagation, feedback mechanisms, multi-chip network, etc.
  32. 32. NEUROMORPHIC CHIPSETS 32 SAMPLE REPORT Patent Trend Analysis: Top Technology Domains – Focus Areas (1/2) Technology Areas Sub-Technology Focus Areas Patent Assignees Neurons Time synchronization, encoding, modulation, replay components, plasticity, analog computation, neuron processors, feedback loops, materials, processing engines Circuit Parallel processing, analog chip, accelerators, non-linear characteristics, reduced chip area, multiplexing of neurosynaptic cores, spike- timing-dependent plasticity Synapses Dynamic processing, nano-sheets, field programmable synapses array, optical synapse, resistance adjustments, weights of synapses, crossbar architecture Fabrication Crossbar arrays of resistive processing units, 3D structures, synapses, multi-level architectures, reduced chip area, ambipolar devices, low-power designs Edge Devices Autonomous systems, robots, low-power requirements, context-based mapping, parallelism, olfactory systems, image recognition, improved computation Memory Resistive control, stochastic operations, thermodynamic RAM, multi-bit characteristics, reconfigurable mapping, accelerator architectures, phase change
  33. 33. NEUROMORPHIC CHIPSETS 33 SAMPLE REPORT Patent Trend Analysis: Assignees across Geographies Top Assignees • IBM • Qualcomm • Intel • SK Hynix Other Key Assignees • Brain Corporation • HRL Laboratories • Samsung • Denso Corporation Research Institutes and Universities • The Regents of the University of California • Northeastern University • MIT • The Charles Stark Draper Laboratory • Boise State University • Stanford University • University of Dayton Emerging Assignees • Applied Brain Research • Brainchip Key Assignees • Erle Robotics • Samsung • HRL Laboratories Research Institutes and Universities • National Center for Scientific Research CNRS • Ecole Polytechnique • Technical University Dresden Key Assignees • Honda Motors • Qualcomm • Intel Research Institutes and Universities • Waseda University Top Assignees • SK Hynix Other Key Assignees • Samsung Research Institutes and Universities • Postech Academy-Industry Foundation • Korea University Research and Business Foundation • Myongji University • Incheon University Key Assignee • IBM Research Institutes and Universities • Tsinghua University • Peking University • Nanjing University • Zhejiang University • Beijing Institute of Technology • Hefei University of Technology
  34. 34. NEUROMORPHIC CHIPSETS 34 SAMPLE REPORT Patent Trend Analysis: Overview of Assignees q Over 190 applicants have filed patents in the neuromorphic hardware domain q The top five assignees account for half of the total patent count, signifying the dominance of major entities in the neuromorphic hardware domain q As the domain is dominated by the big players, patent filing activity of emerging entities is considerably less q The remaining assignees include a mix of semiconductor manufacturers and IP providers, automotive manufacturers and automotive service parts suppliers, universities and research institutes, and specialist neuromorphic solutions providers IBM Qualcomm SKHynix Intel Brain Corporation Remaining 349 out of 700 of the total patent publications are assigned to the top 5 assignees
  35. 35. NEUROMORPHIC CHIPSETS 35 SAMPLE REPORT Patent Trend Analysis: Top Assignees 0 20 40 60 80 100 120 140 160 180 Neurons Circuits Synapse Fabrication Edge Devices Memory Memristors Interconnects Others Number of Patent Publications q IBM is leading the race with a comprehensive patent portfolio covering all aspects of the technology q Qualcomm holds the second place in terms of patent count. However, there is considerable gap between the top two patent holders q More than half of the patent publications assigned to Qualcomm are focused on neurons and related architectures and sub-technologies q SK Hynix’s patent portfolio is primarily focused on synapse- related features, including enhancing symmetry and linearity of synapses, with improved data retention capabilities and new materials q Intel’s IP focuses on accelerator circuits and stochastic operation for reducing delays, and also on reconfigurable and flexible interconnection schemes q Brain Corp. has significant number of patent filings in edge device implementations
  36. 36. NEUROMORPHIC CHIPSETS 36 SAMPLE REPORT Patent Trend Analysis: Emerging Players and Top Universities Exploring carbon nanotubes for implementing dendrite circuits that can emulate neuronal behavior for neuromorphic computing applications Neuromemristive AI and thermodynamic RAM technology stack utilizing Anti-Hebbian and Hebbian (AHaH) computing Low-cost, neuromorphic chipsets and integrated memory modules focused on local, real-time, energy- efficient operations Edge device implementation with focus on voice-based recognition and activation, low-power neuronal designs, and autonomous systems Associative connections between neurons in a neural network for efficient learning and autonomous operations Neural processing units for edge devices with reduction in the number of components used and reduced hardware cost BrainChip has transformed into a specialized neuromorphic chip solution provider that is now competing with the big players in the domain and has a considerably strong patent portfolio to support its position. Apart from BrainChip, other emerging players were also identified focusing on various aspects of the neuromorphic computing domain, including memories, memristors, edge devices, autonomous operations, and new materials for the architecture ……… University, ……. Academy-Industry Foundation, and the Regents of the University of California are the top universities in the domain with the maximum number of patent publications Emerging Players Top Active Universities
  37. 37. SAMPLE REPORT Key Takeaways
  38. 38. Click to edit Master title style NEUROMORPHIC CHIPSETS 38 SAMPLE REPORT Key Takeaways Neuromorphic hardware is still at a relatively nascent stage, companies intending to ……………………….. ……………….ranging from architecture, circuit, and system to materials and fabrication techniques Semiconductor companies looking to enter the neuromorphic space need to start by building chipsets suitable for ……………………….. ………………. and in the future build capabilities for complex computations required for autonomous systems. Companies intending to adopt new business models ……………………….. ………………. opportunities in SNN-based research. Companies considering entry into neural chipsets can consider ……………………….. ………………. NN chipsets and follow it up with SNN-based chips for securing the future applications Semiconductor players can diversify revenue streams, move beyond monetization of silicon, and offer neuromorphic IP licensing Companies providing neuromorphic ……………………….. ………………. advantage compared incumbent semiconductor companies Strategic partnerships with ……………………….. ………………. will allow companies to bring together neuroscience experts and semiconductor designers for developing silicon that can efficiently mimic brain functions Technology Business Partnership Application Neuromorphic chipsets play a critical role in ……………………….. ………………. computational infrastructure. ……………………….. ………………. focusing on neuromorphic chipsets can lead to development in neuromorphic research and future product development.
  39. 39. SAMPLE REPORT Appendix
  40. 40. NEUROMORPHIC CHIPSETS 40 SAMPLE REPORT Definitions • Exascale Computing: Computing systems capable of at least one exaFLOPS (i.e., a quintillion) calculations per second. • Quantum Computing: Application of quantum-mechanical phenomena such as superposition and entanglement to perform computation. • ASICs: A semiconductor design for a specific application that can be programmed to develop custom silicon solutions. • System-on-chip (SoC): An integrated circuit including varied electronic components on a single integrated chip that is used in edge devices such as wearables, smartphones, etc. • Carbon Nanotube: Allotropes of carbon with a cylindrical nanostructure that can be single-walled or multi-walled and finds application in nanotechnology, optics, and electronics. • Backpropagation: A learning algorithm in artificial neural network for the minimum value of the error function in weight space using gradient descent. • Synapses: A structure for information relay between two neurons that is being investigated for building neuromorphic platforms. • Neuromorphic Computing: Engineering approach based on activities of the biological brain. It improves efficiency of complex computational task related to perception and decision making. • Edge Computing: Technique that allows the processing of data close to the device where it is generated instead of contacting any centralized cloud. Edge application services lead to reduction in data volume and the related network traffic. • Neural Network: A network of artificial neurons that uses computational model for information processing to solve artificial intelligence problems. It is used for predictive modeling, adaptive control and other data processing. • Artificial General Intelligence: Representation of generalized human cognitive abilities in software to perform any intellectual task that a human can perform. • Von Neumann Architecture: John von Neumann first published the Von Neurmann Architecture in 1945, where an instruction fetch and data operation occur simulatenously with a shared common bus. • Artificial Super Intelligence: The concept beyond artificial general intelligence where a computer’s cognitive ability surpasses human ability.
  41. 41. NEUROMORPHIC CHIPSETS 41 SAMPLE REPORT Research Methodology The research is focused on key technical trends, solutions, and adoption of the subject technology across sectors. Segregation of the technology focus areas and identification of emerging technology trends and opportunities gives a detailed overview of the technology. Patent study is conducted to provide an overall landscape of the patenting activities in the subject technology. The key trends in the patent filing are identified including assignees, technology focus areas, geographical distribution, and emerging entities. Technology Research Patent Analysis Report Format Report is prepared at the end of the research and is aligned as per expectations and quality check. An in-house editorial team conducts the final level QC. Netscribes follows a structured project management approach to deliver deep-dive research analysis. A comprehensive approach is administered to assess innovative technologies and develop insights related to technology and business. The analysis also includes technical approach taken by the companies to overcome the limitations and assessment of the R&D strategy and future plans Critical insights related to the patent analysis are incorporated to highlight future product development pointers.
  42. 42. NEUROMORPHIC CHIPSETS 42 SAMPLE REPORT Appendix About Netscribes Netscribes is a global market intelligence and content services provider that helps corporations achieve strategic objectives through a wide range of offerings. Our solutions rely on a unique combination of qualitative and quantitative primary research, secondary/desk research, social media analytics, and IP research. For more than 15 years, we have helped our clients across a range of industries, including technology, financial services, healthcare, retail, and CPG. Fortune 500 companies, as well as small- to mid-size firms, have benefited from our partnership with relevant market and competitive insights to drive higher growth, faster customer acquisition, and a sustainable edge in their business.
  43. 43. NEUROMORPHIC CHIPSETS 43 SAMPLE REPORT Disclaimer This report is prepared by Netscribes (India) Private Limited (“Netscribes”), a market intelligence and content service provider The content of this report is developed in accordance with Netscribes’s professional standards. Accordingly, the information provided herein has been obtained from sources which are reasonably believed to be reliable. All information provided in this report is on an “as-is" and an "as-available” basis, and no representations are made about the completeness, veracity, reliability, accuracy, or suitability of its content for any purpose whatsoever. All statements of opinion and all projections, forecasts, or statements relating to expectations regarding future events represent Netscribes’s own assessment and interpretation of information available to it. All liabilities, however arising, in each of the foregoing respects are expressly disclaimed. This report is intended for general information purposes only. This report does not constitute an offer to sell or issue securities, an invitation to purchase or subscribe for securities, or a recommendation to purchase, hold, sell, or abstain from purchasing, any securities. This report is not intended to be used as a basis for making an investment in securities. This report does not form a fiduciary relationship or constitute investment advice. Nothing in this report constitutes legal advice. The information and opinions contained in this report are provided as of the date of the report and are subject to change. Reports may or may not be revised in the future. Any liability to revise any out-of-date report, or to inform recipients about an updated version of such report, is expressly disclaimed. A bonafide recipient is hereby granted a worldwide, royalty-free, enterprise-wide limited license to use the content of this report, subject to the condition that any citation from this report is properly referenced and credited to Netscribes. Nothing herein conveys to the recipients, by implication or by way of estoppel, any intellectual property rights in the report (other than the foregoing limited license) or impairs Netscribes’ intellectual property rights, including but not limited to any rights available to Netscribes under any law or contract. To the maximum extent permitted by law, all liability in respect of this report and any related material is expressly disclaimed. Netscribes does not assume any liability or duty of care for any consequences of any person acting, or refraining to act, by placing reliance on the basis of information contained in this report. All disputes and claims arising in relation to this report will be submitted to arbitration, which shall be held in Mumbai, India under the Indian Arbitration and Conciliation Act. The exclusive jurisdiction of the courts in Mumbai, India applies to all disputes concerning this report and the interpretation of these terms, and the same shall be governed by and construed in accordance with Indian law without reference to the principles of conflict of laws.
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