Neuromorphic Computing and Sensing 2021 - Sample

From Technologies to Markets
© 2021
From Technologies to Markets
Neuromorphic
Computing and
Sensing
Market and Technology
Report 2021
Sample

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TABLE OF CONTENTS - 1/2
o Report objectives and scope 7
o Methodology 10
o 3 slides summary 16
o Executive summary 20
1-Context 48
o Moore law, more than Moore and data explosion
o Limitation of the current paradigm
o Definition
o Main neuromorphic approaches
2-Market forecast 62
o Neuromorphic sensing and computing applications
o Neuromorphic sensing and computing potential benefits
o 2020 - 2035 Neuromorphic sensing and computing in $.
o 2020 - 2035 Neuromorphic sensing and computing, split
by market in %.
o 2020 - 2035 System-level total available market in Munits.
o 2020 - 2035 Neuromorphic technology penetration in %.
o 2020 - 2035 Neuromorphic sensor average selling price in $.
o 2020 - 2035 Neuromorphic processor average selling price in $.
o What is new compared to the 2019 report?
3-Market trends 80
o Mobile and consumer
o Automotive
o Medical
o Industrial
o Datacenter
4-Ecosystem 163
o AI company landscape
o Neuromorphic and in memory computing players – technology
segmentation
o Neuromorphic image sensor player mapping
o Neuromorphic processor player mapping
o Neuromorphic company market positioning
Neuromorphic Computing and Sensing 2021 | Sample | www.yole.fr | ©2021

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TABLE OF CONTENTS - 2/2
5-Neuromorphic technologies 173
o Neuromorphic sensor technology
• Image sensor technology trends
• Event-based neuromorphic image sensors existing products
o Neuromorphic computing technology
• Neuromorphic computing processors key concepts
• Neuromorphic software
• Spiking Neural Networks
• Software readiness and acceptance
• Available processors
• Top 20 patent-holding companies 2019
• Player's description
o Emerging non-volatile memories for neuromorphic
computing
• Neuromorphic in-memory computing concept
• Emerging NVM, ecosystem and roadmap
• Forecast for emerging NVM
• Player's description
• Embedded NVM for In-Memory Computing
o Appendix 249
• AI definition
• Processor definition
• Yole Développement

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SCOPE OFTHE REPORT – ADOPTION AREAS OF NEUROMORPHIC
Market
Telecom &
infrastructure
Industrial
Medical
Automotive
Mobile
and consumer
End system
Machine vision for factory automation
Robotics
Surveillance camera
Prosthetics
Forward camera, In cabin imaging
Laptop PCs,Tablets
Personal assistant, personal robotics,
consumer security camera
Application
Smart city
Medical
ADAS
Client PC
Phone
Logistic and others
Server
Smartphone
Wearables
Home
Smartwatches, earbuds
Drone Consumer drones
Manufacturing
Datacenter
Robotic Vehicle Robotic vehicle cameras
Drone Industrial drones
Yours needs are
out of the report’
scope?
Contact us for a custom study:
In this report we
analyzed 5 end-markets,
13 applications and a
variety of related end-
systems

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SCOPE OFTHE REPORT – PLAYERS & SYSTEM APPROACHES
The report focuses on neuromorphic IC chips for an efficient execution of AI algorithms
AI chips
The goal is to create processor which
allow to run AI algorithm efficiently
Neuroscience-focused/Brain
simulator
The goal is to create processors which allow
to simulate the brain activity and to validate
neuroscientific models. These elements can be
utilized to design the architecture of a future
neuromorphic AI accelerators.
Neuromorphic computing
Processors which are inspired
from the brain
Research driven project
Go to market
approach
Main focus of
the report
Non-exhaustive list
And many others…
Yours needs are
out of the report’
scope?
Contact us for a custom study:
Courtesy of Intel

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• Definition and description of neuromorphic technologies.
• 2020 - 2035 Neuromorphic sensing and computing market forecast in $.
• 2020 - 2035 Neuromorphic technology penetration in %.
• Mobile, automotive, medical, industrial and data center market trends.
• Neuromorphic players – mapping and technology segmentation.
• Available neuromorphic processors and image sensors.
• Neuromorphic software description, software readiness, and acceptance.
• Neuromorphic player's descriptions.
• Neuromorphic in-memory computing concept.
• Emerging non-volatile memory, ecosystem, and roadmap.
• Forecast for emerging non-volatile memory.
• Embedded non-volatile memory for in-memory computing.
KEY FEATURES OF REPORT

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METHODOLOGY AND DEFINITIONS
Market segmentation
volume in Munits,
ASP in $,
revenue in $M
Yole’s market forecast model is based on matching heterogeneous sources:
Information
aggregation
Preexisting
information
Forecast
Reconciliation
END SYSTEM SEMICONDUCTOR DEVICE

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Adrien Sanchez
As a Technology & Market Analyst, Adrien Sanchez belongs to the Computing & Software business unit at Yole Développement (Yole), part of the Yole
Group of Companies. In collaboration with his team, Adrien produces technology & market analyses covering computing hardware and software, AI,
machine learning and neural networks. Prior to Yole, he worked as an intern at AW Europe (Belgium), where he focused on image recognition &
comprehension for ADAS. He also worked at ACOEM (France), where he focused on real-time sound classification using deep learning and edge
computing. Adrien graduated with a double degree at Grenoble Institute of Technology PHELMA (Grenoble INP Phelma, France) and Grenoble Ecole de
Management (GEM, France), and he earned an MSc on AI at Heriot-Watt University (Edinburgh, UK).
Contact: adrien.sanchez@yole.fr
Pierre Cambou
Pierre Cambou MSc, MBA, is a Principal Analyst in the Photonics and Sensing Division atYole Développement (Yole).
Pierre’s mission is dedicated to imaging related activities by providing market & technology analyses along with strategy consulting services to
semiconductor companies. He is responsible for the CIS Quarterly Market Monitor while he has authored more than 15 Yole Market & Technology
reports. Pierre has an Engineering degree from Université de Technologie de Compiègne (France) and a Master of Science from Virginia Tech. (VA, USA),
Pierre also graduated with an MBA from Grenoble Ecole de Management (France).
Contact: pierre.cambou@yole.fr
Biographies & contact
ABOUT THE AUTHORS
Simone Bertolazzi,Ph.D.
Simone is a Sr. Technology & Market analyst at Yole Développement (Yole) working with the Semiconductor & Software division. He is member of Yole’s
memory team and contributes on a day-to-day basis to the analysis of memory markets and technologies, their related materials and fabrication
processes. Previously, Simone carried out experimental research in the field of nanoscience and nanotechnology, focusing on emerging semiconducting
materials and their device applications. He (co-) authored more than 15 papers in high-impact scientific journals and was awarded the prestigious Marie
Curie Intra-European Fellowship. Simone obtained a PhD in physics in 2015 from École Polytechnique Fédérale de Lausanne (Switzerland), where he
developed novel flash memory cells based on heterostructures of 2D materials and high-κ dielectrics.
Simone earned a double M.A. Sc. degree from Polytechnique de Montréal (Canada) and Politecnico di Milano (Italy), graduating cum laude.
Contact: simone.bertolazzi@yole.fr

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ABR,Accenture,Adesto Technologies, ai Storm,Allibaba.com,Amazon,Ambarella,AMD,AMT,
Anotherbrain,Antaïos,Apple,Applied Materials,ARM,AryballeTechnologies,AvalancheTechnology,
Aspinity,AWS, Axis,Azure, Baidu, BMW, Brainchip, Canon, Celepixel, Ceva, Crossbar, Cogito
Instruments, Dahua, Dialog, Facebook, Fraunhofer, GeneralVision, GlobalFoundries, Google, Gorilla,
GrAI Matter Lab, Groq, HPLabs, GyrfalconTechnology, HLMC, Hprobe, Huawei, IBM, IMEC, Infineon,
IniLabs, Inivation, Innatera, Insightness, Intel, Kalray, Knowm, Mediatek, Microchip, Micron, Mythic,
Natural Intelligence, Nepes AI, NeuroMem, Numem, Numenta, Nuvoton, Nvidia, Omnivision,
Prophesee, Qualcomm, Rain Neuromorphics, Robosensing, Samsung, Scheider Electric, Sensigent, SMIC,
Sony, ST Microelectronics, Synsense, Synthara, Syntiant,TDK,TSMC, UMC,Vicarious,Weebit,Westwell
Labs, Xilinx and more…
COMPANIES CITED IN THIS REPORT

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2021 – 2035 – APPLICATION’S ADOPTION OF NEUROMORPHIC TECHNOLOGIES
2025 2030
Smartphone
Industrial drones
Smart home
Wearables
In-cabin cameras
Prosthetics
Security
Industrial cameras
MachineVision for robotics
Consumer drones
ADAS
Server
Industrial Consumer Automotive Medical
Datacenter
Volume
shipments
2021 2035
Robotic
vehicles
From 2025 to 2030: Neuromorphic
applications takes off

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FORECAST 2021-2035

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NEUROMORPHIC INTO AI COMPUTING & SENSING – 2025 – 2030 - 2035 REVENUE
EVOLUTION
99,7%
0,3%
92%
8%
82%
18%
2035
2030
2025
Neuromorphic AI
Non-neuromorphic AI
We expect neuromorphic computing and
sensing will represent between 15% and
20% of total AI computing revenue in 2035
~$20B
~$7B
~$0.2B
*Artificial Intelligence

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MARKET TRENDS – CONSUMER & AUTOMOTIVE

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MARKET TRENDS – MEDICAL & DATACENTER

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MARKET TRENDS - INDUSTRIAL

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ECOSYSTEM

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2021 NEUROMORPHIC SENSING AND COMPUTING ECOSYSTEM
Universities Start-ups
Semiconductor large companies
Sensing
Computing
Non-exhaustive list of companies
Sensing
Computing

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NEUROMORPHIC COMPUTING PROCESSORS KEY CONCEPTS
Parallel
architecture
Cores are distributed and
operate in parallel.
Memory, computation
and communication
integration
All of these components are integrated
together, as it is the case in the brain.
Large scalability
Plasticity
Connection
sparsity
Activation
sparsity
For a specific decision, only a small portion of neurons are
firing. It is neurons which are stimulated by inputs. Cores
are communicating via an on-chip event-driven network. It
is an event-driven behavior at the neuron/core level.
A neuron is connected to only few neurons
and few connection are responsible for the
core of the computation (only for NN based
approaches).
It is a key element for scalability.
Ability to learn online
One neurosynaptic core
represent an artificial neural.
Chips communicate via an inter-chip
interface leading to seamless availability like
the brain’s cortex, enabling the creation of
scalable neuromorphic systems.
Neurosynaptic
core
Neuromorphic key concepts
Hardware related concept Software related concept
Software
representation of
neurons
Clock-less
processors
Algorithms based on neural networks
Event-driven
Time dependency, make computation
only when an event occur.
Digital / Analog
Learning
Adaptability and fault tolerance
Time-series
processing
This is a summary of these key neuromorphic concepts for a neuromorphic chip. It doesn’t mean that each neuromorphic processor implement all these concepts,
as neuromorphic processors can mimic the brain according to several ways. But these concept are often at the hearth of innovations which are making chip
neuromorphic. SNN-based processors are maid following most of these properties and will receive a specific attention.These concepts can be integrated into
processors at different degree, it is not always binary properties. In all cases, a tight integration with software is critical for reaching power efficiency, and this is
particularly true for neuromorphic computing.
As time is an inherent component of spiking
approaches, it open the door for efficient
time series approach (both low power and
high speed and accuracy)

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• The main goal of AI is for an artificial system to be autonomous.
For that, understanding the environment and navigating through it
makes up a large part of the research.
• Machine Learning (ML) is one of the most important fields of AI.
The key idea behind ML is teaching an algorithm how to solve a
problem by showing it examples of the solution solved, without
formally explaining how to solve the solution. Three main types of
learning exist supervised learning, unsupervised learning and
reinforcement learning.
• An (artificial) neural network is a network of simple elements
called neurons and is part of machine learning methods. Neurons
are organized in layers, and when a network has several layers, it is
called a Deep Neural Network (DNN). We are talking about
Deep Learning (DL) when Machine Learning principles are applied
to DNN.
• Spiking Neural Network are a specific type of neural network
which is using temporal spikes to encode the information. It can
be used through ML principles for some applications such as
classification.
• Spiking Neural Network can also be used for non-Machine
Learning application such as SLAM or stochastic CSP.
SPIKING NEURAL NETWORK IS A NEUROMORPHIC SOFTWARE
Artificial Intelligence
Machine
Learning
Neural
Network
SupportVector
Machine
(SVM)
Decision
Trees
Nearest
Neighbor
Spiking
Neural
Network
…
Spiking
Neural
Network
• SLAM
• Evolutionary search
• Stochastic CSP
• …
Software
representation
of neurons
Spiking Neural Network are not the only software model
compatible with neuromorphic computing, but it is the
most spread paradigm in this field.
2nd
generation
deep Neural
Network

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TECHNOLOGY TRENDS – SENSING & COMPUTING

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TECHNOLOGY TRENDS – NONVOLATILE MEMORY

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Contact our
SalesTeam
for more
information
22
Contact our
SalesTeam
for more
information
MachineVision for Industry
and Automation 2021
Emerging Non-Volatile
Memory 2021
Cameras and Computing for
Surveillance and Security 2020
Artificial Intelligence Computing
for Automotive 2020
YOLE GROUP OF COMPANIES RELATED ANALYSES
Yole Intelligence

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System Plus Consulting and PISEO, are pleased to provide
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HOWTO USE OUR DATA?

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