Neuromorphic Chipsets - Industry Adoption Analysis

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The Next Revolution in AI Hardware
May 2019
Neuromorphic Chipsets
Applications, Competitive Intelligence, and Patents Analysis

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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

Click to edit Master title style
NEUROMORPHIC CHIPSETS 3
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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

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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

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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

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Introduction to the Architecture and
Properties

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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.

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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

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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

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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.

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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.

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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

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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

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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

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Industry Adoption of Neuromorphic
Chipsets

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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

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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
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.

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Neuromorphic Chipsets: IoT Ecosystem
Advantages of Neuromorphic Chips in IoT Ecosystem
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
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

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Neuromorphic Chipsets: Financial Services
Advantages of Neuromorphic Chips in Financial Services
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
……….
Neuron chips are suitable
option for predicting
unconventional and high
frequency trading patterns
Faster Computation
……………

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Neuromorphic Chipsets: Cybersecurity
Advantages of Neuromorphic Chips in Cybersecurity
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

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Competitive Intelligence

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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.

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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 …….. ……..

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Neuromorphic Chipsets: Increased Interest in Emerging Entities

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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

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R&D in the Neuromorphic Hardware
Domain

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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.

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Patent Analysis of Neuromorphic
Chipsets

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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.

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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

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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.

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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

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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
Universities
• National Center for Scientific
Research CNRS
• Ecole Polytechnique
• Technical University Dresden
Key Assignees
• Honda Motors
• Qualcomm
• Intel
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
Universities
• Tsinghua University
• Peking University
• Nanjing University
• Zhejiang University
• Beijing Institute of Technology
• Hefei University of Technology

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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

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

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

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.

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.

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.

SAMPLE REPORT
Appendix
About Netscribes
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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
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SAMPLE REPORT
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