BRN Discussion Ongoing
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Dhm
Regular
Debunked by people who don't understand the tech and sensitivity of the lungs when they get the virus. A forced cough still has the alveoli irritation that Covid causes and therefore the algorithms detect. Anyway this isn't the place to comment on other companies.
As you say, the Nanose Brainchip technology will be where much of the future lies.
Slymeat
Move on, nothing to see.
The post I read was written by a medical doctor and that was sufficient for me.
Being a long suffering ResApp shareholder, I did have my rose-tinted glasses firmly on at the time too.
And although I was merely replying to your original post on a technology that does not involve Akida, I do believe it is OK to mention other technologies/companies that may be in competition to technologies that incorporate Akida. That creates a healthy discussion.
Clinical Trial on Volatile Organic Compounds: Nanomaterial-based sensors - Clinical Trials Registry - ICH GCP
Early diagnoses of malignant tumors are pivotal for improving their prognoses. The Exhaled Breath is made up of oxygen, carbon dioxide, nitrogen, water, inert gases and volatile organic compounds (VOCs). Theoretically, the concentration of VOCs in exhalation produced by metabolism in human body...
ichgcp.net
Cancer Diagnoses From Exhaled Breath With Na-nose
Completion date: 31/12/2022
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Well Jeff was clearly talking about analog NNs (ReRAM/MemRistors).
"There are issues with manufacturability and quality."
One of PvdM's great insights eliminated these problems at one stroke - a digital NN that uses standard CMOS manufacturing and binary digital signals. While analog neurons are a close theoretical analogy to real neurons, manufacturing variability is still one of the issues that analog NN makers still have to address because any variations in component dimensions alters the amplitude of the analog signals because the strength of the various current signals are added to determine when the firing threshold is reached. The current strength is determined by the tolerance of the resistors/capacitors. So, by way of an illustrative example only, if there were say a 10% variation in signal strengths, adding just 5 analog signals could produce a chance of up to 50% error in the sum of the signal strengths.
On the other hand, binary digital circuits have a high tolerance for manufacturing variations as each switching element produces either a 1 or a zero output. The supply voltage of a CMOS transistor would be much higher than +/- 10% of the trigger threshold voltage.
As for the test equipment, there are the various image databases against which the performance of NNs can be gauged.
I'll leave it to others, like Socionext, Mercedes, Renesas, MegaChips, Valeo, NASA ... to assess the market uncertainty and economic viability.
I blame ill-conceived KIPs for the siloization of work. In the old days, you could always peek over your neighbour's fence to see what was happening elsewhere. I'm pretty sure Crick & Watson did not have KIPs when they were tossing ideas around in a Cambridge pub.
Yoda
Regular
Throwing the question out there, but how much (if any) revenue from these activities do you think we will see in the next quarterly? Or are we still too early for any significant revenue ?
I'm looking forward to Ford vs Mercedes in a hot lap. Then imagine an entire grid full of autonomous race cars each with AKIDA - perhaps not so fanciful....Who is looking forward to Ford breaking cover and saying they have “Akida Inside”.
Surely they want to be looking as cool and EV competitive as Mercedes soon!
That's sounds about as enjoyable and gonad-fizzy as watching Formula E.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Voago Linkedin 2 days ago.
This made me think about FF's post about Nokia being chosen by NASA to deploy the first LTE/4G communications system on the Moon. I'm certainly no expert but I'm assuming that the hardware that Nokia intend to use will need to be space-hardened. Vorago, Nockia and BrainChip - a match made in heaven (or on the moon) one would think.
(Extract 1 - BrainChip and VORAGO Technologies Agree to Collaborate through the Akida™ Early Access Program)
Louis DiNardo, BrainChip CEO commented, “We are both excited and proud to participate in this Phase I program with VORAGO Technologies and support NASA’s desire to leverage neuromorphic computing in spaceflight applications. The combination of benefits from the Akida neuromorphic processor and a radiation-hardened process brings significant new capabilities to spaceflight and aerospace applications”.
Bernd Lienhard, VORAGO CEO added, “We are thrilled and honored to partner with BrainChip to harness the radiation hardening capabilities of our patented HARDSIL® technology for the Phase I program with NASA. Our ongoing mission of creating components with increased availability and unmatched solutions in aerospace and defense applications paired with the Akida neuromorphic processor will create unprecedented standards moving forward in the industry.”
Klein: The main challenge is the environment. We know how to build the core 4G and LTE systems and how to transmit data wirelessly. The first challenge is all of the space hardening, whether it is for the launch of the rocket, the landing on the Moon, or the extreme mechanical stress that the equipment will be exposed to in terms of shock, vibration, and acceleration.
We also need to harden the equipment for the unique space environment, like the extreme hot and cold temperatures, the vacuum, radiation, and the impact of radiation on the equipment. That is a really difficult problem.
I would say the third challenge comes from the operational environment. We need to be very meticulous about size, weight, and power consumption. We need to worry about integrating our network equipment with the lunar lander and integration with the lunar rover. We need to have a fully operational system that is reliable and robust because we can’t send a technician to the Moon to make configuration changes or reboot the system. The system has to be completely autonomous and we need to be able to remotely monitor, manage, and configure it from mission control on Earth.
brainchipinc.com
www.satellitetoday.com
This made me think about FF's post about Nokia being chosen by NASA to deploy the first LTE/4G communications system on the Moon. I'm certainly no expert but I'm assuming that the hardware that Nokia intend to use will need to be space-hardened. Vorago, Nockia and BrainChip - a match made in heaven (or on the moon) one would think.
(Extract 1 - BrainChip and VORAGO Technologies Agree to Collaborate through the Akida™ Early Access Program)
Louis DiNardo, BrainChip CEO commented, “We are both excited and proud to participate in this Phase I program with VORAGO Technologies and support NASA’s desire to leverage neuromorphic computing in spaceflight applications. The combination of benefits from the Akida neuromorphic processor and a radiation-hardened process brings significant new capabilities to spaceflight and aerospace applications”.
Bernd Lienhard, VORAGO CEO added, “We are thrilled and honored to partner with BrainChip to harness the radiation hardening capabilities of our patented HARDSIL® technology for the Phase I program with NASA. Our ongoing mission of creating components with increased availability and unmatched solutions in aerospace and defense applications paired with the Akida neuromorphic processor will create unprecedented standards moving forward in the industry.”
(Extract 2 - Thierry Klein, head of Enterprise and Industrial Automation Research Lab at Nokia Bell Labs, is addressing the gap between communication technology on the Moon and technology astronauts have access to on Earth. )
VIA SATELLITE: What are the challenges to building a network on the Moon? It sounds like science fiction. How difficult will this be?
Klein: The main challenge is the environment. We know how to build the core 4G and LTE systems and how to transmit data wirelessly. The first challenge is all of the space hardening, whether it is for the launch of the rocket, the landing on the Moon, or the extreme mechanical stress that the equipment will be exposed to in terms of shock, vibration, and acceleration.
We also need to harden the equipment for the unique space environment, like the extreme hot and cold temperatures, the vacuum, radiation, and the impact of radiation on the equipment. That is a really difficult problem.
I would say the third challenge comes from the operational environment. We need to be very meticulous about size, weight, and power consumption. We need to worry about integrating our network equipment with the lunar lander and integration with the lunar rover. We need to have a fully operational system that is reliable and robust because we can’t send a technician to the Moon to make configuration changes or reboot the system. The system has to be completely autonomous and we need to be able to remotely monitor, manage, and configure it from mission control on Earth.
BrainChip and VORAGO Technologies Agree to Collaborate through the Akida™ Early Access Program
Collaboration through the Akida™ Early Access Program agreed on by BrainChip and VORAGO Technologies
brainchipinc.com
Bringing LTE to the Moon: Nokia Exec Talks NASA Tipping Point Contract - Via Satellite -
“Why would astronauts on Earth have access to 5G at home, but not have the same access to the same technologies when they are on the Moon?” Thierry
www.satellitetoday.com
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I recently had a private conversation with another poster on this question and extracting the relevant part the following is my view which I referred to Tony Dawe at the time to be sure I was not completely off the mark and he responded that he saw no reason to change my response:
"When Sean Hehir was interviewed earlier this year he answered Yes to a question about would revenue start to ramp up this year.
I personally have never expected explosive income growth this year but there are stages in a company like Brainchip's development.
1. The first is the development phase and during this phase there is no income and going concern status is determined by capital raises.
2. The second stage is becoming a commercial entity and then the income is a mix of capital raises and some income.
3. The third stage is becoming a going commercial concern where its continued operation is being funded from income and capital raises if required are only for special projects or acquisitions and not to meet day to day operating expenses.
4. The fourth stage is when the income increases beyond simply funding ongoing operations and questions of what shall it do with profits arise.
The messages I have been hearing from Brainchip have communicated to me that we are in the second stage and the third stage will start second half of this year.
I personally had no expectation of explosive revenue occurring this year. What I do expect from Peter van der Made's explosive sales statement is that we will see an increasing number of current EAP's breaking cover and more and more logo's of companies appearing in the Early Adopters and Licensee columns and license fees will be seen in the balance sheet. We will also likely see other companies making announcements similar to Mercedes."
My opinion only DYOR
FF
AKIDA BALLISTA
Too long down rabbit holes for me. What I learned was that Driver Monitoring Systems are likely to be mandated just like seat belts. With the need for low power and privacy I would say Akida is in pole position.
From our website:
The Akida AKD1000 Edge AI processor can also monitor the passenger compartment, checking driver alertness with the aim to reduce human error. Privacy is assured without the need to upload images to the internet.
From our website:
The Akida AKD1000 Edge AI processor can also monitor the passenger compartment, checking driver alertness with the aim to reduce human error. Privacy is assured without the need to upload images to the internet.
D
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"Unlike existing approaches, iSentinel utilizes three stages for anomaly detection with low false alarms: (1) the neuromorphic chip for continuous online monitoring and classification, (2) our EWS algorithms run on the mobile device CPU for closer inspection of alerts, and (3) the offline backend server for group behavior analysis"
HRL Labs have a couple of relevant patents/applications.
US10986113 is directly related to the article.
They do not describe their neurons or NPU, but their custom NN contains 576 neurons, so this does not exclude the use of Akida IP.
As to STDP, they use it and "neuron" in the manner of res ipsa loquiter, something which would displease Ella.
US10986113B2 System for continuous validation and threat protection of mobile applications
equivalent to WO2019147330A1 SYSTEM FOR CONTINUOUS VALIDATION AND PROTECTION OF MOBILE APPLICATIONS
Described is a low power system for mobile devices that provides continuous, behavior-based security validation of mobile device applications using neuromorphic hardware. A mobile device comprises a neuromorphic hardware component that runs on the mobile device for continuously monitoring time series related to individual mobile device application behaviors, detecting and classifying pattern anomalies associated with a known malware threat in the time series related to individual mobile device application behaviors, and generating an alert related to the known malware threat. The mobile device identifies pattern anomalies in dependency relationships of mobile device inter-application and intra-applications communications, detects pattern anomalies associated with new malware threats, and isolates a mobile device application having a risk of malware above a predetermined threshold relative to a risk management policy.
[0094] In one embodiment, a neuromorphic chip 306 is used in the first stage 310 of the system. There are several unique features of the hardware design. First, the hardware 306 computes with spikes 320 (fixed voltage pulses of very narrow width (i.e., on the order of 1-2 ms) rather than analog or digital encoding. This mode of encoding is data agnostic and is orders of magnitude more energy efficient compared to a digital system since it only consumes energy during the generation of spikes 320 . Spiking hardware 306 represents signals based on the inter-spike intervals and, thus, is more area efficient since it requires a single wire to encode and transmit information unlike digital systems. Finally, it is more scalable than pure analog systems as spike based systems only require to transmit the timing but not both timing and amplitude parts of the signal in large scale systems
[0095] Various models and algorithms have been developed that can compute with spikes 320 and, in particular, have shown that these models can perform multimodal pattern clustering and recognition as well as associative memories with high storage capacity. For instance, the on-chip learning capabilities can enable the neuromorphic chip 306 to be deployed in one of three modes of operation: unsupervised learning mode (see Literature Reference No. 33) where there is no human in the loop or ground truth, supervised learning mode (see Literature Reference No. 34) where the user can train the chip 306 to learn (for example the classes of objects) and then deployed after the learning is completed or in a reinforcement learning mode (see Literature Reference Nos. 35 and 40), where the chip 306 receives periodic quality of performance feedback (for example, good, bad, etc.) that enables the neuromorphic chip 306 to adapt and learn on-chip. This on-chip learning capability also offers minimal programming, interfacing and software cost while enabling rapid prototyping possibilities. The inputs to the chip 306 will be in the form of spike trains (element 320 ) encoded offline and then fed to the chip 306 , and the neural network on the chip 306 will process the incoming spikes 320 .
[0096] In this mode of operation, the chip 306 functions as a plastic reservoir where the synapses between neurons in the reservoir adapt the gain on the synapses based on spike timing dependent plasticity (STDP). This process is akin to a nonlinear and high-dimensional projection of sensory data into a spatio-temporal space where the data can be readily separated using linear decision boundaries. More specifically, during training, to realize the linear decision boundaries, the spiking activity of a subset of the neurons in the chip 306 is decoded via the output pads and performs a linear regression based learning operation, wherein the firing rates of the neurons sampled at the output pads are linearly combined to cause an output label neuron to fire.
[0098] ... Each synapse includes a weight adaptation circuit based on STDP. To operate, the chip custom boards are specified. These boards are used to interface the chip 306 with a computer. The boards can be used for initial evaluation of different processing neural networks in the chip 306
[0102] The 576-neuron neuromorphic hardware 306 described above has been utilized to perform learned classification of input signals. FIG. 7 depicts a random neural net as configured on a 576-neuron neuromorphic chip.
########################################################
US9336239 is a much earlier HRL patent which also relates to cyber security, and may have been the original basis for the above, albeit it uses "conventional computer" neural network software, possibly something like BrainChip Studio.
US9336239B1 System and method for deep packet inspection and intrusion detection
The present invention relates to a system for deep packet inspection and intrusion detection. The system uses a pattern matching module receiving as an input a data stream in a neural network. Neurons are activated such that when active, the neuron fires to all connecting output neurons to form a neuron spike, each neuron spike from the assigned neuron to a connecting output neuron having a delay. A delay is associated with each input character in the pattern, such that a position of each input character relative to an end of the pattern is stored in an alphabet-pattern-delay matrix (APDFM). An activation matrix (AM) is used to match each input character with a stored pattern to generate a similarity match and determine if the string of characters is the stored pattern.
[0001] This is a Continuation-in-Part application of U.S. Non-Provisional Application No. 13/358,095, filed on Jan. 25, 2012, and entitled, “Neural Network Device with Engineered Delays for Pattern Storage and Matching,” which is a non-provisional application of U.S. Provisional Application No. 61/501,636, filed on Jun. 27, 2011 and entitled, “Neural Network Device with Engineered Delays for Pattern Storage and Matching.”
[0002] This is ALSO non-provisional patent application of U.S. Provisional Application No. 61/589,666. filed on Jan. 23, 2012, entitled, “System and Method for Deep Packet Inspection and Intrusion Detection.”
[0015] ... For example, the system receives as an input a data stream in a neural network. The data stream has a sequence of characters in a pattern and is received in the neural network such that at each time t, only a single input character is received. Each single input character assigned to a neuron in the neural network. The neuron assigned to the single character is activated such that when active, the neuron fires to all connecting output neurons to form a neuron spike, with each neuron spike from the assigned neuron to a connecting output neuron having a delay. A delay associated with each input character in the pattern is determined, such that a position of each input character relative to an end of the pattern is stored in an alphabet-pattern-delay matrix (APDFM). Finally, using an activation matrix (AM), each input character is matched with a stored pattern to generate a similarity match, such that if the similarity match exceeds a predetermined threshold, the sequence of characters in the input data stream is identified as the stored pattern.
[0061] ... the present invention is directed to an intrusion detection system that is operable for performing Deep Packet Inspection (DPI) at wire speeds in software running on conventional processors. The system uses an underlying detection engine (MagicNet) that is based on the relatively new theory of polychronous spiking neural models. The system uses a new and different paradigm than other recent spiking models and liquid state machines in that the network conduction delays are set to optimize detection of specific known patterns of interest (exact pattern matching). By setting the network conduction delays, the system is adapted to provide a more efficient detection than previous methods. In special purpose parallel hardware or a neuromorphic implementation, the system and method is operable for detecting attack signatures at wire speeds greater than 1 Tbps, and with much lower size, weight, and power than conventional methods.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Voago Linkedin 2 days ago.
View attachment 3297
This made me think about FF's post about Nokia being chosen by NASA to deploy the first LTE/4G communications system on the Moon. I'm certainly no expert but I'm assuming that the hardware that Nokia intend to use will need to be space-hardened. Vorago, Nockia and BrainChip - a match made in heaven (or on the moon) one would think.
(Extract 1 - BrainChip and VORAGO Technologies Agree to Collaborate through the Akida™ Early Access Program)
Louis DiNardo, BrainChip CEO commented, “We are both excited and proud to participate in this Phase I program with VORAGO Technologies and support NASA’s desire to leverage neuromorphic computing in spaceflight applications. The combination of benefits from the Akida neuromorphic processor and a radiation-hardened process brings significant new capabilities to spaceflight and aerospace applications”.
Bernd Lienhard, VORAGO CEO added, “We are thrilled and honored to partner with BrainChip to harness the radiation hardening capabilities of our patented HARDSIL® technology for the Phase I program with NASA. Our ongoing mission of creating components with increased availability and unmatched solutions in aerospace and defense applications paired with the Akida neuromorphic processor will create unprecedented standards moving forward in the industry.”
(Extract 2 - Thierry Klein, head of Enterprise and Industrial Automation Research Lab at Nokia Bell Labs, is addressing the gap between communication technology on the Moon and technology astronauts have access to on Earth. )
VIA SATELLITE: What are the challenges to building a network on the Moon? It sounds like science fiction. How difficult will this be?
Klein: The main challenge is the environment. We know how to build the core 4G and LTE systems and how to transmit data wirelessly. The first challenge is all of the space hardening, whether it is for the launch of the rocket, the landing on the Moon, or the extreme mechanical stress that the equipment will be exposed to in terms of shock, vibration, and acceleration.
We also need to harden the equipment for the unique space environment, like the extreme hot and cold temperatures, the vacuum, radiation, and the impact of radiation on the equipment. That is a really difficult problem.
I would say the third challenge comes from the operational environment. We need to be very meticulous about size, weight, and power consumption. We need to worry about integrating our network equipment with the lunar lander and integration with the lunar rover. We need to have a fully operational system that is reliable and robust because we can’t send a technician to the Moon to make configuration changes or reboot the system. The system has to be completely autonomous and we need to be able to remotely monitor, manage, and configure it from mission control on Earth.
BrainChip and VORAGO Technologies Agree to Collaborate through the Akida™ Early Access Program
Collaboration through the Akida™ Early Access Program agreed on by BrainChip and VORAGO TechnologiesBringing LTE to the Moon: Nokia Exec Talks NASA Tipping Point Contract - Via Satellite -
“Why would astronauts on Earth have access to 5G at home, but not have the same access to the same technologies when they are on the Moon?” Thierry
Also, Boeing is building the core stages for Artemis II and III, which is a bit interesting too.
Oh, and apparently the Space Launch System has 500 sensors. I wonder how many of them are AKIDA?
Space Launch System
Fact Finder
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Hi Alfie
I was unsure from all your previous posts to what extent you understand the topic of neuromorphic computing as you write with such a definite approach to subjects but from this reply it seems you might be helped by the following two items.
The first is taken from Wiki:
“Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering[5] to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.[10] It was developed by Carver Mead[11] in the late 1980s.”
The second is a link to an article about neuromorphic engineering:
The reality is there is no such thing yet as a neuromorphic scientist. The closest thing we have in the world of academia is an Engineer who has made a study of neuromorphic computing. As these articles however make clear mostly in universities the study of neuromorphic computing has been in terms of analogue not digital computing though in the last couple of years I have observed that there are now a few universities that offer engineering degrees that have added a subject that covers to some extent digital spiking neural networks.
The great luck that we have here is that we have a retired highly credentialed engineer with a background in patent law who goes by the name of @Diogenese. Diogenese makes the point that sometimes the discussion surrounding neuromorphic computing is above his pay grade and this is only to be expected as he went to university quite a few years ago now but having said this if it is above his pay grade then 99% of us here have not got a chance of understanding it any better.
The point of my post hence the 50% discount I referred to was that as recently as 2020 a mainstream engineer with Hewlett Packard who obviously from his statements had been taking a solid interest in neuromorphic computing had absolutely no inkling that Brainchip and the AKIDA technology solution existed in the real world as Intellectual Property and that a chip AKD1000 was due for release in October, 2020 the year he wrote his little reply.
I did assume Quora provide accurate numbers so it can be seen from his profile that his responses on that forum which is inhabited by a lot of qualified individuals including engineers in the field of computing have read his thoughts 6.4 million times.
He of course may not know anything at all about neuromorphic computing and is just shooting from the hip but if so it is not because he is not a neuromorphic scientist and goes to illustrate the overall point I was making.
My opinion only DYOR
FF
AKIDA BALLISTA
Last edited:
Fact Finder
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Thanks @Diogenese
So is it correct for me to say they do not have a system that can detect previously unseen cyber security threats as it is trained on a set of known patterns of interest and as such does not match AKIDA's ability in this regard?
FF.
I guess the defenders are always going to be playing catch-up, until we see what PvdM's neural cortex is capable of.
I guess Akida 1000 may be capable of recognizing unseen threats if they have sufficient similarity to known threats, eg something designed to exploit a known security weakness, but may struggle with a "revolutionary" threat.
They have sold all of the Raspberry Pi modules. That’s revenue. They must have sold quite a few PCIe boards. No ideas how many PC Shuttles they sold but perhaps a few. That’s all revenue for this quarter. Given they are selling out of stuff I assume they have ordered another batch of Akida chips from Taiwan. Perhaps a larger batch this time around. Looking forward to the quarterly. Just maybe, maybe, ever so maybe, our distribution partners have been selling Akida like hot cakes, or MegaChips and Renesas are going to surprise us with $$$. As usual I’m going in with high expectations. I have been gazing at Mercedes cars and mansions online this morning.
Fact Finder
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Hi Slade
As you discovered a couple of years ago I am a downramper playing a long game so I will stick to my opinion. Just keep it between ourselves though.
FF
As you discovered a couple of years ago I am a downramper playing a long game so I will stick to my opinion. Just keep it between ourselves though.
FF
Bravo
If ARM was an arm, BRN would be its biceps💪!
Renesas is mentioned as a prominent player offering automotive semiconductors.
GetNews
Published
March 25, 2022
“Trends and Forecast for the Global Automotive Semiconductor Market”
Trends, opportunities and forecast in automotive semiconductor market to 2025 by component type (microcontrollers, integrated circuits, sensors, discrete power, and others), vehicle type (small cars, compact cars, mid-Size cars, large cars, SUVs and crossovers, MPVs, pickups, HCVs, electric vehicles, and others), application type (Powertrain, safety, driver information systems, body electronics chassis, and networking/communication), engine type (ICE vehicles and electric vehicles) and region
Lucintel’s latest market report analyzed that automotive semiconductor provides attractive opportunities for passenger cars, commercial, and electric vehicles. The automotive semiconductor market is expected to reach $53.6 billion by 2025 with a CAGR of 9.2%. In this market, integrated circuit is the largest segment by component type, whereas powertrain and safety is largest by application. The introduction of high efficiency power semiconductors and development of smaller single–chips for radar sensors provides strategic growth path in this market.
Download Brochure of this report by clicking on https://www.lucintel.com/automotive-semiconductor-market.aspx Based on component type, the automotive semiconductormarket is segmented into microcontrollers, integrated circuits, sensors, discrete power, and others. The integrated circuits segment accounted for the largest share of the market in 2020 due to rapid vehicle electrification, increasing demand for electronics, and growth in vehicle production. The automotive sensor segment is expected to witness the highest growth during the forecast period due to the growth in advanced driver assistance system.
Browse in-depth TOC on “Automotive Semiconductor Market”
121 – Tables
145 – Figures
253– Pages
The automotive semiconductor market is marked by the presence of several big and small players. Some of the prominent players offering automotive semiconductor include NXP semiconductor, Renesas Electronics, ST Microelectronics, Infineon Technologies, Texas Instruments, Robert Bosch, On Semiconductor, Micron Technology, TOSHIBA, and Panasonic Semiconductor Solutions and others.
https://www.digitaljournal.com/pr/a...ket-research-report-by-lucintel#ixzz7OnSjygJu
Automotive Semiconductor Market is expected to reach $53.6 Billion by 2025 – An exclusive market research report by Lucintel
ByGetNews
Published
March 25, 2022
“Trends and Forecast for the Global Automotive Semiconductor Market”
Trends, opportunities and forecast in automotive semiconductor market to 2025 by component type (microcontrollers, integrated circuits, sensors, discrete power, and others), vehicle type (small cars, compact cars, mid-Size cars, large cars, SUVs and crossovers, MPVs, pickups, HCVs, electric vehicles, and others), application type (Powertrain, safety, driver information systems, body electronics chassis, and networking/communication), engine type (ICE vehicles and electric vehicles) and region
Lucintel’s latest market report analyzed that automotive semiconductor provides attractive opportunities for passenger cars, commercial, and electric vehicles. The automotive semiconductor market is expected to reach $53.6 billion by 2025 with a CAGR of 9.2%. In this market, integrated circuit is the largest segment by component type, whereas powertrain and safety is largest by application. The introduction of high efficiency power semiconductors and development of smaller single–chips for radar sensors provides strategic growth path in this market.
Download Brochure of this report by clicking on https://www.lucintel.com/automotive-semiconductor-market.aspx Based on component type, the automotive semiconductormarket is segmented into microcontrollers, integrated circuits, sensors, discrete power, and others. The integrated circuits segment accounted for the largest share of the market in 2020 due to rapid vehicle electrification, increasing demand for electronics, and growth in vehicle production. The automotive sensor segment is expected to witness the highest growth during the forecast period due to the growth in advanced driver assistance system.
Browse in-depth TOC on “Automotive Semiconductor Market”
121 – Tables
145 – Figures
253– Pages
The automotive semiconductor market is marked by the presence of several big and small players. Some of the prominent players offering automotive semiconductor include NXP semiconductor, Renesas Electronics, ST Microelectronics, Infineon Technologies, Texas Instruments, Robert Bosch, On Semiconductor, Micron Technology, TOSHIBA, and Panasonic Semiconductor Solutions and others.
https://www.digitaljournal.com/pr/a...ket-research-report-by-lucintel#ixzz7OnSjygJu
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