Summary

The Neuromorphic Chip Market was valued at USD 22.5 million in 2020, and it is projected to be worth USD 333.6 million by 2026, registering a CAGR of 47.4% during the period of 2021-2026. Keeping the pace of advancement of disruptive technologies, such as artificial intelligence (AI) and machine learning (ML), various embedded system providers are keen to develop brain chips, where not only the chips will be processed fast, but will have also responses like human brains for those systems to think and act in a human way.

- Neuromorphic is a specific brain-inspired ASIC that implements the Spiked Neural Networks (SNNs). It has an object to reach the massively parallel brain processing ability in tens of watts on average. The memory and the processing units are in single abstraction (in-memory computing). This leads to the advantage of dynamic, self-programmable behavior in complex environments.
- Companies, such as BrainChip Holdings Ltd, are forming multiple partnership activities to utilize neuromorphic chips in curbing the spread of COVID-19. In May 2021, BrainChip Holdings Ltd partnered with precision immunology company Biotome Pty Ltd, to develop a fast, accurate COVID-19 antibody test. The companies will explore how the Akida neural processor could improve the accuracy and information quality of the antibody-tests while Biotome is developing by providing advanced AI capacity at the point of care.
- Neuromorphic chips can be designed digitally, analog, or in a mixed way. Analog chips resemble the characteristics of the biological properties of neural networks better than digital ones. In the analog architecture, few transistors are used for emulating the differential equations of neurons. Therefore, theoretically, they consume lesser energy than digital neuromorphic chips. Besides, they can extend the processing beyond its allocated time slot. Thanks to this feature, the speed can be accelerated to process faster than in real-time. However, the analog architecture leads to higher noise, which lowers the precision.
- Digital ones, on the other hand, are more precise compared to analog chips. Their digital structure enhances on-chip programming. This flexibility allows artificial intelligent researchers to accurately implement various kinds of an algorithm with low-energy consumption compared to GPUs. Mixed chips try to combine the advantages of analog chips, i.e., lesser energy consumption, and the benefits of digital ones, i.e., precision.
- Neuromorphic architectures address challenges, such as high-power consumption, low speed, and other efficiency-related bottlenecks prevalent in the von Neumann architecture. Unlike the traditional von Neumann architecture with sudden highs and lows in binary encoding, neuromorphic chips provide a continuous analog transition in the form of spiking signals. Neuromorphic architectures integrate storage and processing, getting rid of the bus bottleneck connecting the CPU and memory.

Key Market Trends

Automotive is the Fastest Growing Industry to Adapt Neuromorphic Chip

- The automotive industry is one of the fastest-growing industries for neuromorphic chips. All the premium car manufacturers are investing heavily to achieve Level 5 of Vehicle Autonomy; which in turn, is anticipated to generate huge demand for AI-powered neuromorphic chips.
- The autonomous driving market requires constant improvement in AI algorithms for high throughput with low power requirements. Neuromorphic chips are ideal for classification tasks and could be utilized for several scenarios in autonomous driving. They are also efficient in a noisy environment, such as self-driving vehicles, compared with static deep learning solutions.
- According to Intel, four terabytes is the estimated amount of data that an autonomous car may generate through almost an hour and a half of driving or the amount of time a general person spends in their car each day. Autonomous vehicles face a significant challenge in efficiently managing all the data generated during these trips.
- The computers running the latest self-driving cars are effectively small supercomputers. The companies, such as Nvidia, aim to achieve Level 5 autonomous driving in 2022, delivering 200TOPS (trillions of operations per second) using 750W of power. However, spending 750W an hour on processing is poised to have a noticeable impact on the driving range of electric vehicles.
- Among various automotive applications of neuromorphic chips, ADAS (Advanced Driver Assistance System) applications include image learning and recognition function. It works like one of conventional ADAS functions, such as cruise control or intelligent speed, assist system in passenger cars. It can control vehicle speed by recognizing the traffic information marked on roads, such as crosswalks, school zone, road-bump, etc.

North American is Expected to Hold Major Share over the Forecast Period

- North America is home to some of the major market vendors, such as Intel Corporation and IBM Corporation. nThe market for neuromorphic chips is growing in the region due to factors, such as government initiatives, investment activities, and others. For instance, in September 2020, the Department of Energy (DOE) announced USD 2 million funding for five basic research projects to advance neuromorphic computing. The initiative by DOE supports the development of both hardware and software for brain-inspired neuromorphic computing.
- The miniaturization of neuromorphic chips that help in different applications is also contributing to the market's growth. For instance, in June 2020, MIT engineers designed a brain-on-chip smaller than a piece of confetti, made from tens of thousands of artificial brain synapses known as memristors, which are silicon-based components that mimic the information transmitting synapses in the human brain. Such chips can be utilized in small and portable AI devices.
- Government of Canada is also focusing on Artificial Intelligence technology, which will create a scope for growth in neuromorphic computing over the coming years. For instance, in June 2020, the governments of Canada and Quebec joined hands to advance the responsible development of AI. The focus will be on different themes, such as future work and innovation, commercialization, data governance, and reliable AI.
- Big investments into research and development activities through partnerships are being witnessed in the region. For instance, in October 2020, Sandia National Laboratories, one of three National Nuclear Security Administration research and development laboratories in the United States, partnered with Intel to explore the value of neuromorphic computing for scaled-up computational problems.
- The penetration of neural-based chipsets in commercialized applications is also propelling growth of the market. For instance, in November 2020, one of the biggest technology companies, Apple, launched its M1 Chip explicitly designed for its Mac products. The M1 Chip brings Apple Neural Engine to the Mac, and accelerates the machine learning tasks. The 16-core architecture can perform 11 trillion operations per second, and thereby, enable up to 15x faster ML performance.

Competitive Landscape

As the market for neuromorphic chips is very niche and in the initial phase of development, the market has a presence of a few players, such as BrainChip Holdings Ltd, Intel Corporation, SynSense AG, etc. In this consolidated market scenario, top players are growing intensely through various market development strategies, such as collaboration, market expansion, product innovation, and R&D activities. Hence the market concentration is medium.

- April 2021 – Brainchip Research Institute in Perth has entered into a research collaboration with precision immunology company Biotome Pty Ltd. Biotome is developing highly accurate antibody tests for infections. Brainchip's Akida neuromorphic processor chip will be used to interpret sensor responses and to find out which responses are the most representative for antibodies that are protective.
- January 2021 - GrAI Matter Labs introduced GrAI VIP, Vision Inference Processor, a full-stack AI system-on-chip platform that will drive a significant step in fast responsiveness for visual inference capabilities in robotics, industrial automation, AR/VR, and surveillance products and markets. GrAI Matter Labs’ proven NeuronFlow event-based dataflow compute technology in GrAI VIP enables industry-leading inference latency up-to 100x better than competing solutions.
- September 2020 - SynSense announced that company is building the next generation of brain-machine interfaces. Company set the goal of making a neural prosthesis for a rat, so it can sense the world even without whiskers. This technology will be able to restore touch or other sensations to people who have lost it, in the future.
- March 2020 - SolidRun and Gyrfalcon developed First Edge Optimized AI Inference Server Janux GS31 that supports leading neural network frameworks. It can be configured with up to 128 Gyrfalcon Lightspeeur SPR2803 AI acceleration chips for improved inference performance for most complex video AI models.

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Table of Contents

1 INTRODUCTION
1.1 Study Assumptions & Market Definition
1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS
4.1 Market Overview
4.2 Industry Attractiveness - Porter's Five Forces Analysis
4.2.1 Bargaining Power of Suppliers
4.2.2 Bargaining Power of Consumers
4.2.3 Threat of New Entrants
4.2.4 Threat of Substitutes
4.2.5 Intensity of Competitive Rivalry
4.3 Industry Value Chain Analysis
4.4 Emerging Use Cases for Neuromorphic Chips
4.5 Analysis of the Impact of COVID-19 on the Market
4.6 Market Drivers
4.6.1 Increasing Demand for Artificial Intelligence-based Microchips
4.6.2 Emerging Trend of Combining the Concept of Neuroplasticity with Electronics?
4.7 Market Challenges
4.7.1 Need for High Level of Precision and Complexity in Hardware Design

5 GLOBAL DEEP LEARNING MARKET ANALYSIS
5.1 Current market scenario
5.2 Global Deep Learning Market Segmentation
5.2.1 Type
5.2.1.1 CPU
5.2.1.2 GPU
5.2.1.3 FPGA
5.2.1.4 ASIC
5.2.1.5 SoC Accelerators
5.3 Coverage on the Current Trends in the Deep Learning Software and Service industry
5.4 Investment Scenario
5.5 List of Major Hardware Vendors
5.6 Future of the Market

6 GLOBAL NEUROMORPHIC CHIP MARKET SEGMENTATION
6.1 End User Industry
6.1.1 Financial Services and Cybersecurity
6.1.2 Automotive
6.1.3 Industrial
6.1.4 Consumer Electronics
6.1.5 Other End User Industries
6.2 Geography
6.2.1 North America
6.2.2 Europe
6.2.3 Asia Pacific
6.2.4 Rest of the World

7 COMPETITIVE INTELLIGENCE
7.1 Company Profiles
7.1.1 Intel Corporation
7.1.2 SK Hynix Inc.
7.1.3 IBM Corporation
7.1.4 Samsung Electronics Co. Ltd
7.1.5 GrAI Matter Labs
7.1.6 Nepes Corporation
7.1.7 General Vision Inc.
7.1.8 Gyrfalcon Technology Inc.
7.1.9 BrainChip Holdings Ltd
7.1.10 Vicarious FPC Inc.
7.1.11 SynSense AG

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET