Reality AI appears to be software only so no real competition.
BRN Discussion Ongoing
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Reuben
Founding Member
@Bravo .. if you are on linkedin follow porsche engineering .. the company, that is where i hav been doing the digging to find something linked to brn..Hey MC, I came across an article about the "KI Delta Learning" written in October 2021. It's in German though and the only word I understood was "tool" but thanks to Dr. Google's translation skills, I made my way through some of it that hints at self-learning and have provided the translation of the blue highlighted section.
I JUST REALISED THAT THIS WAS THE EXACT SAME ARTICLE THAT @Reuben POSTED EXCEPT HIS ARTICLE WAS IN ENGLISH! I'M GOING TO LEAVE THIS HEAR AS A REMINDER OF HOW STOOPID I AM.
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KI Delta Learning: der kleine Unterschied
Bei einem Wechsel der Umgebung oder der Sensorik müssen neuronale Netze in Fahrzeugen heute immer wieder von Grund auf neu trainiert werden. Das Forschungsprojekt „KI Delta Learning“ will dieses Problem lösen und so den Aufwand erheblich reduzieren.newsroom.porsche.com
Okeydokey01
Regular
Off the back of this announcement, selfish or not, today I feel proud just for holding Brainchip shares and having this small connection to the company.
You want integrity, trust and generosity there it is. And while it may start with PVDM it doesn't end with him. Through our entire team we are in good hands, morally, technically and professionally. A truly remarkable day to be a shareholder in Brainchip and another reminder that we are invested in a company, not just a share price.
this looks like a software classification system run on a CPU/GPU.
US11170215B1 System and method for discriminating and demarcating targets of interest in a physical scene
Reality Analytics
[0051] A classifier is a set of rules and/or algorithms, executing on a computer processor or other microprocessor-based device or platform, which define how to process data values from samples of a dataset in order to identify the feature represented by said data. A classifier may be “trained” by processing data which is known to represent a particular category of feature and storing the results; these results serve as identifying criteria, and the classifier's rules and algorithms may compare the data of newly processed samples representing an unknown feature with these criteria to determine if the feature is of the same category. In some but not all embodiments, a classifier is trained to identify a specific category of feature and classify an unknown feature as in or out of said category; this category represents the classifier's target of interest.
1. A system for detecting and discriminating a feature of interest merged with other features within a physically transduced scene, the system comprising:
an array generating portion executing on a processor to define a multi-dimensional spatial array containing a plurality of physically transduced samples captured for the scene; and
a target discriminating portion executing on a processor to:
formulate a plurality of classification levels, wherein the plurality of physically transduced samples of the scene are mapped at each of the classification levels into a plurality of unit cells of said classification level, the unit cells of different classification levels encompassing spatial regions of different size within the multi-dimensional spatial array,
apply a plurality of predefined classification schemes at the respective classification levels to generate level-specific classifications for the unit cells thereof, the predefined classification schemes of at least two classification levels being different and mutually independent in execution upon different samples captured from the same portion of the scene,
combine the level-specific classifications for the unit cells across the plurality of classification levels to adaptively construct at least one cluster of spatially-contiguous cluster cells based thereon, the cluster cells each being of a common preselected spatial region size independent of classification level, the at least one cluster at least partially defining the feature of interest in peripheral contour within the scene, and,
trigger a detection signal corresponding to discrimination of the feature of interest within the scene,
wherein the combining of level-specific classifications includes, for each cluster cell within the at least one cluster:
combining the level-specific classifications for the unit cells of each respective classification level contained within the cluster cell to generate a level-specific classification of the cluster cell, and,
combining each level-specific classification of the cluster cell to generate a general classification of the cluster cell.
Akida could do the classification standing on its head.
D
Deleted member 118
Guest
What’s So Exciting About Neuromorphic Computing
The human brain is the most efficient and powerful computer that exists. Even after decades and decades of technological advancements, no computer has managed to beat the brain with respect to efficiency, power consumption, and many other factors.
www.electronicsforu.com
What’s So Exciting About Neuromorphic Computing
The human brain is the most efficient and powerful computer that exists. Even after decades and decades of technological advancements, no computer has managed to beat the brain with respect to efficiency, power consumption, and many other factors.
What’s So Exciting About Neuromorphic Computing
By Aaryaa PadhyegurjarMay 18, 2022
110
Telegram
https://www.electronicsforu.com/technology-trends/exciting-neuromorphic-computing/amp#
The human brain is the most efficient and powerful computer that exists. Even after decades and decades of technological advancements, no computer has managed to beat the brain with respect to efficiency, power consumption, and many other factors.
Will neuromorphic computers be able to do it?
The exact sequence of events that take place when we do a particular activity on our computer, or on any other device, completely depends on its inherent architecture. It depends on how the various components of the computer like the processor and memory are structured in the solid state.
Fast forward to today
Nowadays, almost all companies have dedicated teams working on neuromorphic computing. Groundbreaking research is being done in multiple research organisations and universities. It is safe to say that neuromorphic computing is gaining momentum and will continue to do so as various advancements are being made.
What’s interesting to note is that although this is a specialised field with prerequisites from various topics, including solid-state physics, VLSI, neural networks, and computational neurobiology, undergraduate engineering students are extremely curious about this field.
At IIT Kanpur, Dr Shubham Sahay, Assistant Professor at the Department of Electrical Engineering, introduced a course on neuromorphic computing last year. Despite being a post-graduate level course, he saw great participation from undergrads as well. “Throughout the course, they were very interactive. The huge B.Tech participation in my course bears testimony to the fact that undergrads are really interested in this topic. I believe that this (neuromorphic computing) could be introduced as one of the core courses in the UG curriculum in the future,” he says.
Getting it commercial
Until recently, neuromorphic computing was a widely used term only in research and not in the commercial arena. However, as of January 18, 2022, BrainChip, a leading provider of ultra-low-power high-performance AI technology, commercialised its AKD1000 AIOT chip. Developers, system integrators, and engineers can now buy AKD1000-powered Mini PCIe boards and leverage them in their applications, especially those requiring on-edge computing, low power consumption, and high-performance AI.
“It’s meant as our entry-level product. We want to proliferate this into as many hands as we can and get people designing in the Akida environment,” says Rob Telson, Vice President of WorldWide Sales at BrainChip. Anil Mankar, Co-founder and Chief Development Officer of BrainChip, explains, “We are enabling system integrators to easily use neuromorphic AI in their applications. In India, if some system integrators want to manufacture the board locally, they can take the bill of materials from us (BrainChip) and manufacture it locally.”
What’s fascinating about Akida is that it enables sensor nodes to compute without depending on the cloud. Further, BrainChip’s AI technology not only performs audio and video based learning but even focuses on other sensor modalities like taste, vibration, and smell. You can use it to make a sensor that performs wine tasting! Here is a link to their wine tasting demonstration: Link
Another major event that occurred this year was when Mercedes implemented BrainChip’s Akida technology in its Vision EQXX electric vehicle. This is definitely a big deal since the Akida technology is tried and tested for a smart automotive experience. All features that the Akida provides, including facial recognition, keyword spotting, etc consume extremely low power.
“This is where we get excited. You’ll see a lot of these functionalities in vehicles—recognition of voices, faces, and individuals in the vehicle. This allows the vehicles to have customisation and device personalisation according to the drivers or the passengers as well,” says Telson. These really are exciting times.
Neuromorphic hardware, neural networks, and AI
The process in which neurons work is eerily similar to an electric process. Neurons communicate with each other via synapses. Whenever they receive input, they produce electrical signals called spikes (also called action potentials), and the event is called neuron spiking. When this happens, chemicals called neurotransmitters are released into hundreds of synapses and activate the respective neurons. That’s the reason why this process is super-fast.Artificial neural networks mimic the logic of the human brain, but on a regular computer. The thing is, regular computers work on the Von Neumann architecture, which is extremely different from the architecture of our brain and is very power-hungry. We may not be able to deploy CMOS logic on the Von Neumann architecture for long. We will eventually reach a threshold to which we can exploit silicon. We are nearing the end of Moore’s Law and there is a need to establish a better computing mechanism. Neuromorphic computing is the solution because neuromorphic hardware realises the structure of the brain in the solid-state.
As we make progress in neuromorphic hardware, we will be able to deploy neural networks on it. Spiking Neural Network (SNN) is a type of artificial neural network that uses time in its model. It transmits information only when triggered—or, in other words, spiked. SNNs used along with neuromorphic chips will transform the way we compute, which is why they are so important for AI.
How to get started with SNNs
Since the entire architecture of neuromorphic AI chips is different, it is only natural to expect the corresponding software framework to be different too. Developers need to be educated on working with SNNs. However, that is not the case with MetaTF, a free software development framework environment that BrainChip launched in April 2021.“We want to make our environment extremely simple to use. Having people learn a new development environment is not an efficient way to move forward,” says Telson. “We had over 4600 unique users start looking at and playing with MetaTF in 2021. There’s a community out there that wants to learn.”
India and the future of neuromorphic computing
When asked about the scope of neuromorphic computing in India, Dr Sahay mentions, “As of now, the knowledge, dissemination, and expertise in this area is limited to the eminent institutes such as IITs and IISc, but with government initiatives such as India Semiconductor Mission (ISM) and NITI Ayog’s national strategy for artificial intelligence (#AIforall), this field would get a major boost. Also, with respect to opportunities in the industry, several MNCs have memory divisions in India—Micron, Sandisk (WesternDigital), etc—that develop the memory elements which will be used for neuromorphic computing.” There’s a long way to go, but there is absolutely no lack of potential. More companies would eventually have their neuromorphic teams in India.
Fact Finder
Top 20
It went from 9.5 cents to 18.5 cents. The thing is that these announcements or events that is relationships with Nvidia and AWS have some value to Rocketboots the thing about 'ramping' it can be done by false information being released or factual information being released in a way that gives a false impression as to its significance. I have just looked at the price again and it is now down 32.35% further evidencing to the ASX that had the full import of the relationships with Nvidia and AWS been disclosed then the price would not have run to 18.5 cents meeting the definition of ramping.
I will be very surprised if the ASX does not take some form of action but we will see as this seems to me to be an easy target for them and a way to send a strong message to the rest of the ASX participants.
My opinion only DYOR
FF
AKIDA BALLISTA
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supersonic001
Regular
its getting there ......... i think i can ..... !
Fullmoonfever
Top 20
Hey FFI think he or she is on safe ground saying this as they use entirely different approaches and systems so are not compatible one with the other. Put'em together and what have you got? A mess. No bipperty bobbity boo.
But @DingoBorat there have been a number of publications including from the US Defence Department pointing out the shortfalls with these two different technologies and even this morning the post put up by @Rocket577 included a paper out of IMEC which had the following to say:
II. COMPARISON WITH OTHER DIGITAL NEUROMORPHIC
PL ATFORMS
To the best of our knowledge, the SpiNNaker architecture
[3] is a closest neuromorphic platform to SENeCA. SpiNNaker
contains several ARM cores as the processing units connected
through an advanced single router star-type multicasting asyn-
chronous packet-switched network. SpiNNaker2 [4] added
several accelerated arithmetic processing units and advanced
power management techniques in the GF22nm technology
node. On the contrary, SENeCA uses one of the smallest open-
source RISC-V processors as the controller (not used for event
processing) together with optimized accelerators and a low-
overhead mesh-type multicasting NoC (with reduced func-
tionality compared to SpiNNaker) for sparse parallel event-
based computation. Unlike SpiNNaker which is designed for
the simulation of brain-inspired research, the primary purpose
of SENeCA is to have both the hardware and software open
for optimizations and innovations in the EdgeAI neuromorphic
computation.
On the other hand, IBM TrueNorth [5] uses a plain mesh
packet-switched network (uni-cast) but with optimized (inflex-
ible) processing cores. Each core in the TrueNorth architecture
emulates exactly 256 neurons. Each neuron has 256 input
synapses, organized in a crossbar architecture, with a single
output axon connected to 256 neurons in another core. This
optimized processing core resulted in a power-efficient neuron
update (about 26pJ). µBrain [6] goes further in optimized
processing core and allows for ultra-low-power application-
specific IP (in contrast with the multi-purpose neuromorphic
processor).
In Intel Loihi [7], the processing cores are more flexible than
TrueNorth, and the interconnect is a simple uni-cast packet-
switched mesh. Also, Loihi cores accelerate a bio-inspired
learning algorithm. The cost of this flexibility is having a
higher neuron update energy (about 80pJ) in comparison
with the TrueNorth (while using a better technology node).
Loihi2 [8] scaled up the Loihi chip by packing more neurons
and synapses in a die, using the Intel4 technology node.
Additionally, it introduced programmable neurons with micro-
code, a feature also available in SENeCA. Both Loihi chips
accelerate a specific kind of bio-inspired learning mechanism
on-chip."
(PDF) SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture
PDF | SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications... | Find, read and cite all the research you need on ResearchGatewww.researchgate.net
The other thing to note about IBM's True North is it makes no claims to having application at the Edge that is not the territory it is trying to mark out for itself and still maintains it is in research.
Intel's Loihi 1 & 2 are continuously described as only research chips and in the latest release from Intel posted here Intel has stated recently that they are still to identify a use case for Loihi and it may never be produced as a commercial chip and be utilised in the cloud.
If someone wants to use a neuromorphic commercial chip off the shelf at the edge the three major players SpinNaker, IBM and Intel having nothing available in their catalogue so they are compelled to look elsewhere. Brainchip's AKIDA is the undisputed most versatile neuromorphic edge chip on the market today and can also be bought as IP.
Blind Freddie just cannot believe that the sighted people cannot see the bleeding obvious particularly when the following is there in public view for all to see:
1. Nvidia is partnered with Mercedes
2. Brainchip is partnered with Mercedes
3. SiFive is partnered with Nvidia for RISC-V
4. Brainchip is partnered with SiFive to bring Ai to RISC-V
5. Brainchip is partnered with MegaChips for automotive
6. Brainchip’s Rob Telson stated in answer about competing with Nvidia that they see Nvidia more as a partner in the future
7. Nviso is partnered with Brainchip
8. Nviso is working in robotics and is partnering with Brainchip specifically for this purpose
9. Nviso is partnered with Panasonic for robotics
10. MegaChips is also partnered with Brainchip for Industrial Robotics.
11. Brainchip is partnered with Valeo
12. Valeo is partnered with Mercedes for LiDAR
13. Valeo is partnered with Honda for LiDAR
Personally I do not believe anyone knows anymore than we do.
I believe they are just starting to catch up.
My anonymous opinion only so DYOR
FF
AKIDA BALLISTA
Hadn't read too much about SENeCA and only a little on IMEC but just came across this paper and searched TSE for references and saw your post.
Thoughts anyone on the abstract or they just a research competitor of sorts?
Their one page TinyML Presso from Mar 22 attached also.
(PDF) SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture
PDF | SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications... | Find, read and cite all the research you need on ResearchGate
www.researchgate.net
SENeCA: Scalable Energy-efficient Neuromorphic Computer Architecture
- June 2022
- Conference: AICAS 2022
Abstract
SENeCA is our first RISC-V-based digital neuromorphic processor to accelerate bio-inspired Spiking Neural Networks for extreme edge applications inside or near sensors where ultra-low power and adaptivity features are required. SENeCA is optimized to exploit unstructured spatio-temporal sparsity in computations and data transfer. It is a digital IP, that contains interconnected Neuron Cluster Cores, with a RISC-V-based instruction set, an optimized Neuromorphic Co-Processor, and an event-based communication infrastructure. SENeCA improves state of the art by Addressing the flexibility issue in neuromorphic processors by allowing a fully programmable neuron model and learning/adaptivity algorithms; Improving the area efficiency by employing a 3-level memory hierarchy which allows using novel embedded memory technologies; Efficient deployment of advanced learning mechanisms and optimization algorithms by accelerating neural operations in three data types: int4, int8 and BrainFloat16; Efficient event communication by using a new Network-on-Chip with multicasting, a compression mechanism, and source-based routing. The implemented digital IP can be tuned for different applications to have a flexible number of cores and Neural Processing Elements (NPEs) per core and optional use of off-chip memory. Next to the hardware, the SENeCA platform includes an SDK and a hardware-aware simulator for close-loop synthesis/mapping optimization 1.Fact Finder
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Thankyou all who responded to my previous post .Last night I secured an appointment with my mental health specialist who has a masters in séances . I was put in contact with my Godmothers father Tom Luxton who I was told had a chairon the Melbourne Stock Exchange in the 50s . He advised me to change direction in my action by having a tote of my favourite tipple and to seek out a medium that is in contact with a very astute man known as FF . He will advise you to fear not and have a plan and whilst I listen to him the gates of hell will not prevail .So , I am taking that advice and will top up my BRN holding when I can .
I cannot say how much this action on the part of Peter van der Made means to me at a personal level.You great man ... well done PVDM ... and God bless
BrainChip – Director Share Donation Sydney, Australia– June 09, 2022 – ASX: BRN, OTCQX: BRCHF, ADR: BCHPY BrainChip Holdings Ltd, the world’s first commercial producer of ultra-low power neuromorphic AI IP, today announced that BrainChip founder and Chief Technology Officer Peter van der Made has made a personal donation of 3,500,000 BrainChip Ordinary Shares to the Lions Alzheimer’s Foundation in Australia. Mr van der Made said, “This donation is intended to provide funding for the continued research of Dr Ralph Martins and his team at the Lions Alzheimer’s Foundation in Perth. Alzheimer’s Disease is an insidious affliction that robs patients of their memories and their quality of life. My father had Alzheimer’s Disease and his suffering impacted me and my family profoundly. I’ve dedicated my professional life to the study of the human brain, so this donation is my way of contributing to the ongoing research to find a cure for this terrible disease.” The share donation was approved by the BrainChip board, and the shares were transferred through Share Gift Australia, where they have been sold on market in five tranches of 700,000 shares per tranche, over a five (5) day period between 03 June 2022 and 08 June 2022. Based on the share price at the time of the sale, the sales have raised approximately $3.6 million dollars for the Lions Alzheimer’s Foundation. Mr van der Made is personally paying all taxes associated with the donation of shares to the Lions Alzheimers Foundation. Dr Ralph Martins said, “I am deeply grateful to Peter for making this generous donation to the Lions Alzheimer’s Foundation to support and advance our research programs for the early Diagnosis, Prevention and Treatment of Alzheimer’s disease. In my 38 year career working on Alzheimer’s disease research this is the single largest donation second only to Malcolm McCusker and his family who funded the establishment of my research team over several years. We are on the cusp of major breakthroughs for Alzheimer’s and Dementia and this wonderful gift from Peter will hopefully lead to other generous donors coming forward to enable us to develop a blood test for the early detection of Alzheimer’s and implement effective prevention programs for the wider community.” This announcement is authorised for release by the BRN Board of Directors.
About 10 years ago now a truly wonderful and equally humble man who spent his entire life in the service of the community through his work as a specialist physician in the public hospital system retired and within six months was diagnosed with an aggressive form of dementia and within a further 8 months passed away. He was fully aware what the diagnosis held instore and was heart broken as he knew only too well what this was likely to mean to his family and those who needed and wanted to care for him. While he was not aware of it the violent aggressive person he became was such a terrible outcome to the gentle caring life he had lived.
I think it would be a fitting tribute to Peter van der Made that when the share price reaches $5.00 that we all take the opportunity to make a further donation in his name based upon our personal circumstances to the Lions Alzheimer's Foundation in Perth.
My opinion only DYOR
FF
AKIDA BALLISTA
Fact Finder
Top 20
By the way Peter van der Made's father was a chemical engineer and was insistent that Peter study chemical engineering. Peter was equally determined to follow his own path and at the age of 16.5 years left home and travelled to Australia and the rest is history as they say.
Imagine what would not have occurred if Peter had stayed and become a chemical engineer instead of pursuing his passion.
My opinion only DYOR
FF
AKIDA BALLISTA
Imagine what would not have occurred if Peter had stayed and become a chemical engineer instead of pursuing his passion.
My opinion only DYOR
FF
AKIDA BALLISTA
Mugen74
Regular
I retired a few years ago to fulltime care for my parents due to dementia (mainly Dad)At a certain point we had to put my father in a home (unfortunately as I dont have the medical skills nor equipment required for the level of care needed.Still looking after Mum as she is only showing mild symtoms.Dad doesnt remember me anymore
.Its heartbraking to go through(Im tearing up as I write this).Im just thankfull for all the dedicated staff/ people that work in the homes to give suffers some kind of quality of life.Hopefully a cure will be found soon
D
Deleted member 118
Guest
3.8 Akida
Akida [39] is the first commercial neuromorphic processor, commercially available since August 2021. It has been developed by Australian BrainChip since 2013. Fifteen companies, including
15
NASA, joined the early access program. In addition to Akida System on Chip (SoC), BrainChip also offers licensing of their technologies, providing chip manufacturers a license to build custom solutions.
The chip is marketed as a power efficient event-based processor for Edge computing, not requiring an external CPU. Power consumption for various tasks may range from 100 μW to 300 mW. For example, Akida is capable of processing at 1,000 frames/Watt (compare to TrueNorth with 6,000 frames/Watt). The first generation chip supports operations with convolutional and fully connected networks, with the prospect to add support of LSTM, transformers, capsule networks, recurrent and cortical neural networks. ANN network can be transformed into SNN and executed on the chip.
One Akida chip in a mesh network incorporates 80 Neural Processing Units (NPU), which enables modeling 1,200,000 neurons and 10,000,000,000 synapses. The chip is built at TSMC 28 nm. In 2022, BrainChip announced the second generation chip at 16 nm.
Akida’s ecosystem provides a free chip emulator, TensorFlow compatible framework MetaTF for transformation of convolutional and fully connected neural networks into SNN, аnd a set of pre-trained models. When designing a neural network architecture for execution at Akida, one should take into account a number of additional limitations concerning the layer parameters (e.g. maximum convolution size is 7, while stride 2 is supported for convolution size 3 only) and their sequence.
The major distinctive feature is that incremental, one-shot and continuous learning are sup- ported straight at the chip. At the AI Hardware Summit 2021 BrainChip showed the solution capable of identifying a human in other contexts after having seen him or her only once. Another product by BrainChip is a smart speaker, that on having heard a new voice asks the speaker to identify and after that calls the person by their name. There results are achieved with help of a proprietary local training algorithm on the basis of homeostatic STDP. Only the last fully connected layer supports synaptic plasticity and is involved in learning.
Another instructive case from the AI Hardware Summit 2021 was a classification of fast- moving objects (for example, a race car). Usually, such objects are off the frame center and significantly blurred but they can be detected using an event-based approach
Dolci
Regular
below is the broker data from 01/06 to 03/06
below is the broker data from 03/05 to 03/06
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buena suerte :-)
BOB Bank of Brainchip
"I think it would be a fitting tribute to Peter van der Made that when the share price reaches $5.00 that we all take the opportunity to make a further donation in his name based upon our personal circumstances to the Lions Alzheimer's Foundation in Perth." (FF)
Put me on the list FF, .... A wonderful idea and as like so many I have a family member suffering with this awful disease.
Mccabe84
Regular
Thanks, much appreciated. Would I be right in saying Goldman Sachs have accumulated a fair chunk ?
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Bravo
If ARM was an arm, BRN would be its biceps💪!
Hi Chippers,
This is a MUST READ article about sensor fusion / combining data from different souces such sensors, cameras, GPS, etc. I particularly liked the part highlighted below. Oh, and just so everyone is aware - Siemens EDA are involved in the IFS Accelerator Program, along with ARM and SiFive.
IMO it's quite likely the partner companies in the IFS Accelerator Program will aim to create and drive the "defacto standard" which is mentioned in the article.
Who’s Involved in the IFS Accelerator Program: The IFS Accelerator features innovative partner companies across each of the three pillars of the program:
semiengineering.com
This is a MUST READ article about sensor fusion / combining data from different souces such sensors, cameras, GPS, etc. I particularly liked the part highlighted below. Oh, and just so everyone is aware - Siemens EDA are involved in the IFS Accelerator Program, along with ARM and SiFive.
IMO it's quite likely the partner companies in the IFS Accelerator Program will aim to create and drive the "defacto standard" which is mentioned in the article.
Who’s Involved in the IFS Accelerator Program: The IFS Accelerator features innovative partner companies across each of the three pillars of the program:
- EDA Alliance: Ansys, Cadence, Siemens EDA, Synopsys
- IP Alliance: Alphawave, Analog Bits, Andes, Arm, Cadence, eMemory, M31, SiFive, Silicon Creations, Synopsys, Vidatronic
- Design Services Alliance: Capgemini, Tech Mahindra, Wipro
Competing Auto Sensor Fusion Approaches
Cost, data volume, and complexity drive multiple solutions.
semiengineering.com
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Earlyrelease
Regular
Whilst PVDM is a true gentleman for donating this I do hope for the Institute’s sake they didn’t sell the entire holding. Surely on receipt of such a gift they may ask Peter the value of holding onto at least half the shares, to one day make their initial share sale looks like an annual dividend cheque
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