Is Akida possibly in Renesas latest release.
BRN Discussion Ongoing
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toasty
Regular
I have postulated a number of times over the last 2 monhs that one or more partners may already be in possession of Akida with LSTM.....given Prophesee's statements about Akida being the missing piece of their jigsaw puzzle its hard to now imagine that the LSTM model they refer to is not AKIDA 2.0..........just my observation so make sure you DYOR
ndefries
Regular
Smartphone cameras will outperform DSLRs in three to five years: Qualcomm VP
Smartphone cameras are already a lot more popular than professional cameras. But they usually fail to offer the sheer image ...www.sammobile.com
View attachment 18549
I wonder what the camera will look like?
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cosors
👀
Akida and Talnode meet at MB that much is certain
I wonder if the Renesas licence for Akida is transferrable.Maybe another dot to follow
EnOcean accelerates its growth and breadth of energy-saving solutions through the acquisition of the assets of the edge computing solutions business from Renesas
EnOcean accelerates its growth and breadth of energy-saving solutions through the acquisition of the assets of the edge computing solutions business from Renesas
EnOcean, the pioneer of energy harvesting IoT devices and sensor-to-cloud solutions, is announcing the acquisition of the assets of the edge computing solutions business from the Renesas Electronics Corporation. The deal includes the transition of personnel as well as hardware and software...www.design-reuse.com
I don't know how many chips made on American machines are sold to China, but, if there is any lingering backlog from the chip famine, this should clear that up.Just a reminder before you read the following:
1. Brainchip has absolutely no debt - none - zero - and money in the bank.
2. Brainchip made the decision in 2020, probably for security reasons not to engage with China so has no sales pipeline into China that is disrupted.
3. Only the ignorant will lump Brainchip in with the rest of the semiconductor industry as it is now part of the BIG BANK Index:
LATEST FINANCE NEWS
Semiconductors weigh on Nasdaq: All eyes on US reporting season
11 Oct 2022 - Stocks declined on Monday with the Nasdaq Composite index falling to the lowest level in two years as tech shares, which are sensitive to interest rate hikes, continue to be the hardest hit in this bear market. Shares of semiconductor companies fell overnight, with the industry selling off globally after fresh US curbs on China’s access to American technology added to a disappointing start to the earnings season, stoking concern that the industry's downturn is far from over.
My opinion only DYOR
FF
AKIDA BALLISTA
Space Cadet
Regular
Don't know but here is link to White Paper on Renesas DRP-AI
https://www.renesas.com/us/en/document/whp/embedded-ai-accelerator-drp-ai?language=en&r=1630841
SC
That's a well thought out post.
Excellent stuff! Many shareholders would clearly understand the merit in what you have mentioned.
About 13 business days left until our 4C fronts up to deliver what we all hope for, that is, steady as she goes, any revenue close to the last quarter will be a good sign, any increase would be fantastic, anyone interested in a game of two-up
equanimous
Norse clairvoyant shapeshifter goddess
relevant
Boab
I wish I could paint like Vincent
General sentiment is pessimistic but no need to be when it comes to BrainChip. GLTAH
Stable Genius
Regular
Interesting to see what our partner Prophesee is up to:
TheDrooben
Pretty Pretty Pretty Pretty Good
I'm in the other 51.1%......
Fact Finder
Top 20
This is an important find generously shared by @Bravo.
It is firstly important to note this presentation took place on 7 February, 2022 about 9 months ago.
During the presentation Q&A Prophesee CEO Luca Verre states they have been partnered with:
1. Intel Loihi,
2. GraiMatter, and
3. SynSense
looking for a neural processor that would complete their Event based sensor technology.
Based upon Brainchip Podcast No. 20 with Luca Verre it is a reasonable conclusion that all three failed to meet Prophesee’s requirements.
The other fact I drew out of this presentation was that for many use cases Prophesee’s EVent Based Sensor will need to work with other sensors and sensor fusion will need to be available.
The most common use case will be a conventional frame based camera being combined with Prophesee’s Event Based Sensor which as we know is the type of sensor fusion AKIDA technology thrives on.
Indeed Luca Verre also mentioned fusion of Prophesee’s Event Based Sensor with Lidar, Radar and Infra Red and we know once again AKIDA is already proven and ready to roll.
My opinion only DYOR
FF
AKIDA BALLISTA
DingoBorat
Slim
Between the 3rd and the 4th of October, the shorters covered more than 3 million shares, making very decent coin.
Very disappointed, to see some soft serves here, gifting cash to these A holes..
Short position has since increased.
BrainChip can and will, weather and outperform the markets!
Shorters are not a charitable organisation!
But if fear fills your hearts about the future, by looking at the present World macro stage..
Go ahead and gift them your shares
...
Very disappointed, to see some soft serves here, gifting cash to these A holes..
Short position has since increased.
BrainChip can and will, weather and outperform the markets!
Shorters are not a charitable organisation!
But if fear fills your hearts about the future, by looking at the present World macro stage..
Go ahead and gift them your shares
...Attachments
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Violin1
Regular
Will be fun to see @TECH - but I'm still betting low revenue until the following quarter. The wick must be burning though, if we're to explode in the next six months! I would be beside myself with excitement if we saw the R word in the 4c, regardless of how small the figure against it was.That's a well thought out post.
Excellent stuff! Many shareholders would clearly understand the merit in what you have mentioned.
About 13 business days left until our 4C fronts up to deliver what we all hope for, that is, steady as she goes, any revenue close to the last quarter will be a good sign, any increase would be fantastic, anyone interested in a game of two-up
R=royalty
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Fullmoonfever
Top 20
@Diogenese or anyone else more techy.....opinion or education pls
The BRN patent below was one filed in 2019 and whilst we all know of SNN I see references to SCNN as a spiking convolutional neural network which we've also heard of.
I noticed the acronym used within some NetApp documentation in relation to AI, NVDA examples etc. (some snips & links below) and mused whether their use of the acronym is similar. They call it Spatial Convolutional Neural Network and relates to autonomous driving and other uses but will stick to AD for this purpose. They do reference it across to GPUs as well, so is it something that is purely GPU driven by NetApp files, embedded or something else.
My question is:
Within our patent I also see references to the spatial nature of the spikes and presume NetApp's use of the term is purely around the "spatial" effect within CNN but given we know we can convert CNN to SNN?
Are these 2 distinctly separately terms / uses or could there be an overlap as we know the industry all uses various acronyms to suit their own discussions, products but are essentially one and the same or similar?
Are we able to be integrated cloud side?
But conventional SNNs can suffer from several technological problems. First, conventional SNNs are unable to switch between convolution and fully connected operation. For example, a conventional SNN may be configured at design time to use a fully-connected feedforward architecture to learn features and classify data. Embodiments herein (e.g., the neuromorphic integrated circuit) solve this technological problem by combining the features of a CNN and a SNN into a spiking convolutional neural network (SCNN) that can be configured to switch between a convolution operation or a fully-connected neural network function. The SCNN may also reduce the number of synapse weights for each neuron. This can also allow the SCNN to be deeper (e.g., have more layers) than a conventional SNN with fewer synapse weights for each neuron. Embodiments herein further improve the convolution operation by using a winner-take-all (WTA) approach for each neuron acting as a filter at particular position of the input space. This can improve the selectivity and invariance of the network. In other words, this can improve the accuracy of an inference operation.
docs.netapp.com
The elements used in this solution are:
In this architecture, the focus is on the most computationally intensive part of the AI or machine learning (ML) distributed training process of lane detection. Lane detection is one of the most important tasks in autonomous driving, which helps to guide vehicles by localization of the lane markings. Static components like lane markings guide the vehicle to drive on the highway interactively and safely.
Convolutional Neural Network (CNN)-based approaches have pushed scene understanding and segmentation to a new level. Although it doesn’t perform well for objects with long structures and regions that could be occluded (for example, poles, shade on the lane, and so on). Spatial Convolutional Neural Network (SCNN) generalizes the CNN to a rich spatial level. It allows information propagation between neurons in the same layer, which makes it best suited for structured objects such as lanes, poles, or truck with occlusions. This compatibility is because the spatial information can be reinforced, and it preserves smoothness and continuity.
Thousands of scene images need to be injected in the system to allow the model learn and distinguish the various components in the dataset. These images include weather, daytime or nighttime, multilane highway roads, and other traffic conditions.
For training, there is a need for good quality and quantity of data. Single GPU or multiple GPUs can take days to weeks to complete the training. Data-distributed training can speed up the process by using multiple and multinode GPUs. Horovod is one such framework that grants distributed training but reading data across clusters of GPUs could act as a hindrance. Azure NetApp Files provides ultrafast, high throughput and sustained low latency to provide scale-out/scale-up capabilities so that GPUs are leveraged to the best of their computational capacity. Our experiments verified that all the GPUs across the cluster are used more than 96% on average for training the lane detection using SCNN.
NetApp, Inc. is an American hybrid cloud data services and data management company headquartered in San Jose, California. It has ranked in the Fortune 500 from 2012–2021. Founded in 1992 with an IPO in 1995, NetApp offers cloud data services for management of applications and data both online and physically.
The BRN patent below was one filed in 2019 and whilst we all know of SNN I see references to SCNN as a spiking convolutional neural network which we've also heard of.
I noticed the acronym used within some NetApp documentation in relation to AI, NVDA examples etc. (some snips & links below) and mused whether their use of the acronym is similar. They call it Spatial Convolutional Neural Network and relates to autonomous driving and other uses but will stick to AD for this purpose. They do reference it across to GPUs as well, so is it something that is purely GPU driven by NetApp files, embedded or something else.
My question is:
Within our patent I also see references to the spatial nature of the spikes and presume NetApp's use of the term is purely around the "spatial" effect within CNN but given we know we can convert CNN to SNN?
Are these 2 distinctly separately terms / uses or could there be an overlap as we know the industry all uses various acronyms to suit their own discussions, products but are essentially one and the same or similar?
Are we able to be integrated cloud side?
US20200143229A1 - Spiking neural network - Google Patents
Disclosed herein are system, method, and computer program product embodiments for an improved spiking neural network (SNN) configured to learn and perform unsupervised extraction of features from an input stream. An embodiment operates by receiving a set of spike bits corresponding to a set...
patents.google.com
But conventional SNNs can suffer from several technological problems. First, conventional SNNs are unable to switch between convolution and fully connected operation. For example, a conventional SNN may be configured at design time to use a fully-connected feedforward architecture to learn features and classify data. Embodiments herein (e.g., the neuromorphic integrated circuit) solve this technological problem by combining the features of a CNN and a SNN into a spiking convolutional neural network (SCNN) that can be configured to switch between a convolution operation or a fully-connected neural network function. The SCNN may also reduce the number of synapse weights for each neuron. This can also allow the SCNN to be deeper (e.g., have more layers) than a conventional SNN with fewer synapse weights for each neuron. Embodiments herein further improve the convolution operation by using a winner-take-all (WTA) approach for each neuron acting as a filter at particular position of the input space. This can improve the selectivity and invariance of the network. In other words, this can improve the accuracy of an inference operation.
- In some embodiments, an input to a SCNN is derived from an audio stream. An Analog to Digital (A/D) converter can convert the audio stream to digital data. The A/D converter can output the digital data in the form of Pulse Code Modulation (PCM) data. A data to spike converter can convert the digital data to a series of spatially and temporally distributed spikes representing the spectrum of the audio stream.
- [0052]
In some embodiments, an input to a SCNN is derived from a video stream. The A/D converter can convert the video stream to digital data. For example, the A/D converter can convert the video stream to pixel information in which the intensity of each pixel is expressed as a digital value. A digital camera can provide such pixel information. For example, the digital camera can provide pixel information in the form of three 8-bit values for red, green and blue pixels. The pixel information can be captured and stored in memory. The data to spike converter can convert the pixel information to spatially and temporally distributed spikes by means of sensory neurons that simulate the actions of the human visual tract.
Solution overview
In this architecture, the focus is on the most computationally intensive part of the AI or machine learning (ML) distributed training process of lane detection.
The elements used in this solution are:
- Azure Kubernetes Service (AKS)
- Azure Compute SKUs with NVIDIA GPUs
- Azure NetApp Files
- RUN: AI
- NetApp Trident
In this architecture, the focus is on the most computationally intensive part of the AI or machine learning (ML) distributed training process of lane detection. Lane detection is one of the most important tasks in autonomous driving, which helps to guide vehicles by localization of the lane markings. Static components like lane markings guide the vehicle to drive on the highway interactively and safely.
Convolutional Neural Network (CNN)-based approaches have pushed scene understanding and segmentation to a new level. Although it doesn’t perform well for objects with long structures and regions that could be occluded (for example, poles, shade on the lane, and so on). Spatial Convolutional Neural Network (SCNN) generalizes the CNN to a rich spatial level. It allows information propagation between neurons in the same layer, which makes it best suited for structured objects such as lanes, poles, or truck with occlusions. This compatibility is because the spatial information can be reinforced, and it preserves smoothness and continuity.
Thousands of scene images need to be injected in the system to allow the model learn and distinguish the various components in the dataset. These images include weather, daytime or nighttime, multilane highway roads, and other traffic conditions.
For training, there is a need for good quality and quantity of data. Single GPU or multiple GPUs can take days to weeks to complete the training. Data-distributed training can speed up the process by using multiple and multinode GPUs. Horovod is one such framework that grants distributed training but reading data across clusters of GPUs could act as a hindrance. Azure NetApp Files provides ultrafast, high throughput and sustained low latency to provide scale-out/scale-up capabilities so that GPUs are leveraged to the best of their computational capacity. Our experiments verified that all the GPUs across the cluster are used more than 96% on average for training the lane detection using SCNN.
NetApp, Inc. is an American hybrid cloud data services and data management company headquartered in San Jose, California. It has ranked in the Fortune 500 from 2012–2021. Founded in 1992 with an IPO in 1995, NetApp offers cloud data services for management of applications and data both online and physically.
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DingoBorat
Slim
People, do need to understand that, as FactFinder has already outlined previously, Peter's predictions of an explosive growth in sales and any Company "guidance" of this happening towards the end of this year, was before Putin's aggression and terrorism, towards Ukraine and all the World problems this has caused.
This doesn't mean we won't still get it, but people should temper their expectations...
I'd rather be surprised to the upside, than disappointed..
If royalties do get a mention, as having started to flow, which I think is a very real possibility, I don't think we will ever see, a break up of it in revenues..
Good Fortune to all Holders!
Slava Ukraini!
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