BRN Discussion Ongoing
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HopalongPetrovski
I'm Spartacus!
I have just noticed that Nabtrade is not showing if transactions are cross trades or any of that type of info today?
Not sure how long it's been absent.
Are other platforms still routinely showing this info when you look at the course of trades?
Not sure how long it's been absent.
Are other platforms still routinely showing this info when you look at the course of trades?
Afternoon HoppalongPetrovski,
Comsuck is displaying all trades, codes.
Wether it's an accurate depiction of what's actually happening is debatable.
. Regards,
Esq.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Maybe some further connection here to dig into with BrainCHip + Tata + Ipsolon + Renasas because of mutual interests in radio frequency stuff.
Funny how the same names keep cropping up all over the place.
Makeme 2020
Regular
RENESAS SHOWCASING NEW PRODUCTS AT EMBEDDED WORLD MARCH 14 -16
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jtardif999
Regular
Don’t think so, all of the companies listed in the scroll comments have been happy to tell everyone they love BRN they are not hiding behind NDAs. Leaves all those who have NDAs still same same..
Reuben
Founding Member
Thanks for posting it @TechGirl .... huge news.... x280 processor from sifive is something i will keep my eyes open from now.. now that we know it is with google...Awesome thanks, you beat me to it.
A good Friend of mine Reuben asked me to post the following screenshots from a facebook group plus the article.
Here are the facebook screenshots
View attachment 31404
View attachment 31403
Here is he Google SIFive X280 article
Google deploys SiFive's Intelligence X280 processor for AI workloads
Hybridizes the RISC-V cores with TPU architecture
September 22, 2022 By Sebastian Moss Have your say
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Google is using the RISC-V-based SiFive Intelligence X280 processor in combination with the Google TPU, as part of its portfolio of AI chips.
Fabless chip designer SiFive said that it was also being used by NASA, Tenstorrent, Renesas, Microchip, Kinara, and others.
RISC-V is an open standard instruction set architecture based on established RISC principles, which is provided under open source licenses that do not require fees.
– SiFive/Google
At the AI Hardware Summit in Santa Clara, Krste Asanovic, SiFive's co-founder and chief architect took to the stage with Cliff Young, Google TPU Architect and MLPerf co-founder.
The SiFive Intelligence X280 is a multi-core capable RISC-V processor with vector extension, optimized for AI/ML applications in the data center.
At the summit, the two companies explained that the X280 processor is being used as the AI Compute Host to provide flexible programming combined with the Google MXU (systolic matrix multiplier) accelerator in the data center. However, they did not disclose the scale of the deployment.
SiFive has introduced the Vector Coprocessor Interface eXtension (VCIX), allowing customers to plug an accelerator directly into the vector register file of the X280.
Google already uses third-party ASIC design services with Broadcom for its in-house TPU AI chip, instead focusing on developing its strengths - the Matrix Multiply Unit and the Inter-Chip Interconnect.
Now it is adding the X280 in what Google calls "an elegant division of labor with the TPU," taking the MXUs and combining them with the X280.
Google's Cliff Young said that with SiFive VCIX-based general purpose cores “hybridized” with Google MXUs, you can build a machine "that lets you have your cake and eat it too."
Fullmoonfever
Top 20
Just revisited this research article I skimmed mid last year.
Only cause discussed SNN and had Infineon and BMW authors amongst the others haha
Too technical for me but maybe @Diogenese could have a skim?
Reason for revisit is I recalled some of the new capabilities or similar that is in our new platform being discussed.
Scene / Semantic Segmentation, Spatial Temporal etc and also get a note reference at one section to Thorpes rank order coding.
They discuss some of these needs and the benefits for radar in ADAS.
They discuss Spinnaker and Loihi at this time however I suspect the new Akida functionalities could assist?
www.frontiersin.org
Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges
Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.
5.3. Toward Neuromorphic Automated Driving
As motivated in the introduction, the use of neuromorphic hardware has a high potential to significantly reduce the energy demands for highly-automated driving. Besides radar signals, also camera and LIDAR data need to be processed in order to get a complete understanding of the automotive scene. For image processing there already exist first attempts to solve complex tasks with SNNs, e.g., for object detection (Kim et al., 2020) or semantic segmentation (Kim et al., 2021). Also recently, Viale et al. (2021) realized an SNN on Loihi for car detection using a dynamic vision sensor. Using LIDAR data, which is naturally sparse and thus predestined for SNNs, Zhou et al. (2020) showed a spiking convolutional network for real-time 3D object detection. Shalumov et al. (2021) use LIDAR data for SNN-based collision avoidance with a control network based on the neural engineering framework. All these examples show that SNN-based sensor processing for autonomous driving is a trending topic. Besides the development of SNNs and their implementation on neuromorphic hardware, also the combined processing, i.e., sensor fusion using SNNs, will become an important topic.
When it comes to AI-based autonomous driving, ensuring functional safety of both software and hardware is a critical issue. The principles that are currently developed to support machine learning models (Henriksson et al., 2018; Mohseni et al., 2019) will also apply to SNNs. Similarly, neuromorphic hardware will have to fulfill the same standards as any automotive electronic system: adhere to temperature ranges, be resistant to vibrations, be deterministic and redundant, or contain self-monitoring. For that reason, only digital neuromorphic systems are candidates for integration in cars, while the use of analog or mixed-signal neuromorphic hardware seems out of scope at the moment due to their intrinsic variability. Hence, we suggest to focus on advanced digital systems such as SpiNNaker2 (Yan et al., 2021) or Loihi2 (Orchard et al., 2021) to further explore neuromorphic hardware for automotive radar processing and automated driving in general.
Only cause discussed SNN and had Infineon and BMW authors amongst the others haha
Too technical for me but maybe @Diogenese could have a skim?
Reason for revisit is I recalled some of the new capabilities or similar that is in our new platform being discussed.
Scene / Semantic Segmentation, Spatial Temporal etc and also get a note reference at one section to Thorpes rank order coding.
They discuss some of these needs and the benefits for radar in ADAS.
They discuss Spinnaker and Loihi at this time however I suspect the new Akida functionalities could assist?
Frontiers | Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges
Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light c...
www.frontiersin.org
Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges
Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.
5.3. Toward Neuromorphic Automated Driving
As motivated in the introduction, the use of neuromorphic hardware has a high potential to significantly reduce the energy demands for highly-automated driving. Besides radar signals, also camera and LIDAR data need to be processed in order to get a complete understanding of the automotive scene. For image processing there already exist first attempts to solve complex tasks with SNNs, e.g., for object detection (Kim et al., 2020) or semantic segmentation (Kim et al., 2021). Also recently, Viale et al. (2021) realized an SNN on Loihi for car detection using a dynamic vision sensor. Using LIDAR data, which is naturally sparse and thus predestined for SNNs, Zhou et al. (2020) showed a spiking convolutional network for real-time 3D object detection. Shalumov et al. (2021) use LIDAR data for SNN-based collision avoidance with a control network based on the neural engineering framework. All these examples show that SNN-based sensor processing for autonomous driving is a trending topic. Besides the development of SNNs and their implementation on neuromorphic hardware, also the combined processing, i.e., sensor fusion using SNNs, will become an important topic.
When it comes to AI-based autonomous driving, ensuring functional safety of both software and hardware is a critical issue. The principles that are currently developed to support machine learning models (Henriksson et al., 2018; Mohseni et al., 2019) will also apply to SNNs. Similarly, neuromorphic hardware will have to fulfill the same standards as any automotive electronic system: adhere to temperature ranges, be resistant to vibrations, be deterministic and redundant, or contain self-monitoring. For that reason, only digital neuromorphic systems are candidates for integration in cars, while the use of analog or mixed-signal neuromorphic hardware seems out of scope at the moment due to their intrinsic variability. Hence, we suggest to focus on advanced digital systems such as SpiNNaker2 (Yan et al., 2021) or Loihi2 (Orchard et al., 2021) to further explore neuromorphic hardware for automotive radar processing and automated driving in general.
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robsmark
Regular
I think this proves that without deals being signed with guarantees of near-term revenue, or proven revenue from the existing two contracts, we aren’t going anywhere.
Shorts will continue to devalue this company until the company provides the market with further proof of adoption.
Non-material announcements don’t get much better than yesterdays, but unfortunately it didn’t generate the financial traction required to reverse the trend.
It is crucial that some of these EAP customers show some commitment to Brainchip by signing a deal. I see no reason why this shouldn’t be the case either given how the company has already announced that changes make to this latest revision were directly requested by the EAP customers.
More time is required I guess.
Rise from the ashes
Regular
And maybe throw in some nefarious forces with deep pockets who are pressuring prices down to accumulate.
Which imo is a very good possibility.
robsmark
Regular
That’s just wishful thinking until proven though unfortunately Rise.
Those institutions which have accumulated thus far are questionable to me also - someone is making a fortune lending them to shorters, how do we know this isn’t their strategy with this company?
Rise from the ashes
Regular
I doubt that would ever be able to be proven though so yes really pointless of me to have mentioned it.
Those shorts will eventually have to unwind which will logically move the price up. Yes logic seems to be non-existent these days. But like you mentioned in a previous post a little more time is needed.
Not that it means shit but my confidence level is pretty high that this is going to rock hard this year mate. And had that same feeling before we got the sugar hit yesterday. Lots of new info to dissect from that. I'll cut it short before I start to ramble on.
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Rise from the ashes
Regular
And... Ramble I shall
Think of all the companies that are working with brn then think of all the employees who work with then then think of all the friends and associates connected to them oh boy he comes the tin foil quite a few of them could be collaborating to keep prices down for accumulation. And that's it for my ramblings. Before I bring UFOs into the post
Because we all know pvdm is a time traveler from the future.
Think of all the companies that are working with brn then think of all the employees who work with then then think of all the friends and associates connected to them oh boy he comes the tin foil
quite a few of them could be collaborating to keep prices down for accumulation. And that's it for my ramblings. Before I bring UFOs into the postBecause we all know pvdm is a time traveler from the future.
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Fact Finder
Top 20
SiFive and BrainChip Partner to Demo IP Compatibility
SiFive and BrainChip have partnered to show their IP is compatible in SoC designs for embedded artificial intelligence (AI). The companies have demonstrated BrainChip’s neuromorphic processing unit (NPU) IP working alongside SiFive’s RISC–V host processor IP.
Brainchip’s NPU processor IP, the basis for its Akida chip, is a neuromorphic processor designed to accelerate spiking neural networks. This IP can be used to analyze inputs from most sensor types, including cameras, to provide ultra–low power analysis in real–time applications. A recent BrainChip demo showed its Akida chip in a vehicle, detecting the driver, recognizing the driver’s face, and identifying their voice simultaneously. Keyword spotting required 600 μW, facial recognition needed 22 mW, and the visual wake–word inference used to detect the driver was 6–8 mW
SiFive and BrainChip have partnered to show their IP is compatible in SoC designs for embedded artificial intelligence (AI). The companies have demonstrated BrainChip’s neuromorphic processing unit (NPU) IP working alongside SiFive’s RISC–V host processor IP.
Brainchip’s NPU processor IP, the basis for its Akida chip, is a neuromorphic processor designed to accelerate spiking neural networks. This IP can be used to analyze inputs from most sensor types, including cameras, to provide ultra–low power analysis in real–time applications. A recent BrainChip demo showed its Akida chip in a vehicle, detecting the driver, recognizing the driver’s face, and identifying their voice simultaneously. Keyword spotting required 600 μW, facial recognition needed 22 mW, and the visual wake–word inference used to detect the driver was 6–8 mW
Fact Finder
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Product Category
Microcontrollers & Microprocessors
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Tothemoon24
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This paper has an author who likes second generation AKIDA:
My opinion only DYOR
FF
AKIDA BALLISTA
My opinion only DYOR
FF
AKIDA BALLISTA
equanimous
Norse clairvoyant shapeshifter goddess
Ok people any guesses for New performance variant with NVISO?
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