BRN Discussion Ongoing
- Thread starter TechGirl
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BrainChip is trusted by Valeo. It says so on our website. They gotta be trusting us with something.It's been 2 years 5 months and 22 days since the agreement seems like a longtime to some but Rome was not built in a day as each day passes the closer we are to being unveiled imo.
BrainChip signs joint development agreement for Akida neuromorphic chip with Valeo
Artificial intelligence device company BrainChip Holdings (ASX: BRN) has taken an affirmative step towards integrating its Akida neuromorphic chip into autonomous vehicles after signing a binding joint development agreement with European automotive supplier Valeo Corporation.smallcaps.com.au
Rise from the ashes
Regular
And one day millions of people will trust us with their safety.
White Horse
Regular
How's this for Bull Shit !!!
Course of Sales
Course of sales table| Time | Price $ | Volume | Value $ | Market | Condition | |
|---|---|---|---|---|---|---|
| 12:59:50 PM | 0.735 | 1,222 | 898.170 | ASX | ||
| 12:58:33 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:32 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:30 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:29 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:26 PM | 0.735 | 2 | 1.470 | ASX | ||
| 12:58:24 PM | 0.735 | 2 | 1.470 | ASX | ||
| 12:58:22 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:21 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:20 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:16 PM | 0.735 | 4 | 2.940 | ASX | ||
| 12:58:12 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:10 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:09 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:08 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:58:05 PM | 0.735 | 7 | 5.145 | ASX | ||
| 12:57:57 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:57:56 PM | 0.735 | 2 | 1.470 | ASX | ||
| 12:57:54 PM | 0.735 | 2 | 1.470 | ASX | ||
| 12:57:52 PM | 0.735 | 2 | 1.470 | ASX | ||
| 12:57:48 PM | 0.735 | 3 | 2.205 | ASX | ||
| 12:57:45 PM | 0.735 | 1 | 0.735 | ASX | ||
| 12:57:41 PM | 0.735 | 11 | 8.085 | ASX | ||
| 12:57:25 PM | 0.735 | 4 | 2.940 | ASX | ||
| 12:57:18 PM | 0.735 | 12 | 8.820 | ASX | ||
| 12:57:00 PM | 0.735 | 13 | 9.555 | ASX |
One day I am going to talk to Akida and tell her about the early days and how she was loved by thousands. That we all had her back.
Afternoon White Horse,
Must be a big fish , Berkshire Hathaway , executing a iceberg order.
Regards,
Esq.
Rise from the ashes
Regular
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Neither can he.
Rise from the ashes
Regular
Just a reminder I'm giving away a 1 month subscription to TSE for one lucky member.
thestockexchange.com.au
New giveaway for 1 month subscription to TSE
Hi as with last giveaway post your screen name for a chance to win 1 month subscription. Will be drawn sometime after close tomorrow (if Akida next gen is not announced tomorrow)😂
thestockexchange.com.au
Fact Finder
Top 20
Just a short extract from the article in case others are having trouble getting it to open:
Neuromorphic TinyML
TinyML (tiny machine learning) is all about executing ML and NNs on tightly memory/processor constrained devices such as microcontrollers (MCUs). As a result, it’s a natural step to incorporate a neuromorphic core for TinyML use cases due to several distinct advantages.Neuromorphic devices are event-based processors operating on non-zero events. Event-based convolution and dot products are significantly less computationally expensive since zeroes aren’t processed.
Event-based convolution performance improves further with the larger number of zeroes in the filter channels or kernels. This along with activation functions such as Relu being centered around zero provides the property of event-based processors’ inherent activation sparsity, thus reducing effective MAC requirements.
Furthermore, as a neuromorphic device’s process spikes, more constrained quantization can be used, such as 1-,2- and 4-bit quantization, versus the conventional 8-bit quantization on ANNs. Moreover, because SNNs are incorporated into hardware, neuromorphic devices (such as Akida from Brainchip) have the unique capability of on-edge learning.
That’s not possible with conventional devices. They only simulate a neural network with Von Neumann architecture, leading to on-edge learning being computationally expensive with large memory overheads in a TinyML systems budget. In addition, to train a NN model, integers would not provide enough range to train a model accurately. Therefore, training with 8 bits isn’t currently feasible on traditional architectures.
For traditional architectures, a few on-edge learning implementations with machine-learning algorithms (autoencoders, decision trees) have reached a production stage for simple real-time analytics use cases, whereas NNs are still under research.
To summarize, the advantages of using neuromorphic devices and SNNs at the endpoint include:
- Ultra-low power consumption (millijoule to microjoule per inference)
- Lower MAC requirements as compared to conventional NNs
- Lower parameter memory usage as compared to conventional NNs
- On-edge learning capabilities
Neuromorphic TinyML Use Cases
Microcontrollers with neuromorphic cores can excel in use cases throughout the industry (Fig. 3) thanks to their distinct characteristics of on-edge learning, such as:- In anomaly-detection applications for existing industrial equipment, using the cloud to train a model is inefficient. Adding an endpoint AI device on the motor and training on the edge would allow for ease of scalability, as equipment aging tends to differ from machine to machine even if they’re the same model.
- In robotics, as time passes, the joints of robotic arms tend to wear down, becoming untuned and stop operating as needed. Re-tuning the controller on the edge without human intervention mitigates the need to call a professional, reducing downtime and saving time and money.
- In face-recognition applications, a user would have to add their face to the dataset and retrain the model on the cloud. With a few snaps of a person’s face, the neuromorphic device can identify the end-user via on-edge learning. Thus, users’ data can be secured on the device, and there’s a more seamless experience. This can be employed in cars, where different users have different preferences on seat position, climate control, etc.
- In keyword-spotting applications, extra words can be added to your device to recognize on the edge. It can be used in biometric applications, where a person would add a “secret word” that they would want to keep secure on the device.
Renesas Electronics
3. These represent some edge-computing learning use cases for neuromorphic devices.
3. These represent some edge-computing learning use cases for neuromorphic devices.
The balance of ultra-low-power neuromorphic endpoint devices and enhanced performance makes them suitable for prolonged battery-powered applications, executing algorithms not possible on other low-power devices due to them being computationally constrained (Fig. 4). Or they can be applied to higher-end devices capable of similar processing power that’s too power-hungry. Use cases include:
- Smartwatches that monitor and process the data at the endpoint, sending only relevant information to the cloud.
- Smart camera sensors for people detection to execute a logical command. For instance, automated door opening when a person is approaching, as current technology is based on proximity sensors.
- Area with no connectivity or charging capabilities, such as in forests for smart animal tracking or monitoring under ocean pipes for any potential cracks using real-time vibration, vision, and sound data.
- For infrastructure monitoring use cases, where a neuromorphic MCU can be used to continuously monitor movements, vibrations, and structural changes in bridges (via images) to identify potential failures.
Renesas Electronics
4. These use cases can be implemented using ultra-low-power solutions with high performance using SNNs.
4. These use cases can be implemented using ultra-low-power solutions with high performance using SNNs.
On this front,
References
1. “Neuromorphic computing market –industry analysis, size, share, growth, trends, and forecast, 2020-2028,”
2. “
3. “
4. “
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Deadpool
hyper-efficient Ai
Izzzzzzzzzzy
Regular
Why is this market manipulating itself, US is very green on close, ASX opened very green now everything is literally back down or even in red or just up a small pip.
alwaysgreen
Top 20
If every stock always followed the US markets, every trader would be loaded.
MaxwellAnne
Regular
On relection I am still puzzled as to why Edge Impulse have Akida at the high only when we know it can operate at ultra low power. I would have thought the arrow for Brainchip should cover the whole spectrum.
mrgds
Regular
Thnx for posting @Lex555 ..............
Many might remember the period when RT was always mentioning "BENEFICIAL AI", Apart from a "integration with the fast evolving world of Ai, Neuralink aims to restore abilities of those with mental/physical dissabilities.
Should i, like the 1000 eyes be a little bit excited when hearing Neuralink is using 28nm SoC with a integrated Arm Cortex M23 core ?
The emphasise on low power consumption and thermal control while using CNN/SNN neuromorphic compute certainly got me "a little bit chubbed up " .......
AKIDA ( off to the hot tub ) BALLISTA
Rise from the ashes
Regular
"while using CNN/SNN neuromorphic compute"Thnx for posting @Lex555 ..............
Many might remember the period when RT was always mentioning "BENEFICIAL AI", Apart from a "integration with the fast evolving world of Ai, Neuralink aims to restore abilities of those with mental/physical dissabilities.
Should i, like the 1000 eyes be a little bit excited when hearing Neuralink is using 28nm SoC with a integrated Arm Cortex M23 core ?
The emphasise on low power consumption and thermal control while using CNN/SNN neuromorphic compute certainly got me "a little bit chubbed up " .......
AKIDA ( off to the hot tub ) BALLISTA
Can you point to where you saw that regarding neuralink?
In NABTrade announcements.
Dec 1 (Reuters) -
* TSMC PLANS TO MAKE MORE ADVANCED CHIPS IN US AT URGING OF APPLE- BLOOMBERG NEWS Source text [https://bloom.bg/3EV9lTl]
((Reuters.Briefs@thomsonreuters.com)
Wonder who could make chips "smarter"?
Dec 1 (Reuters) -
* TSMC PLANS TO MAKE MORE ADVANCED CHIPS IN US AT URGING OF APPLE- BLOOMBERG NEWS Source text [https://bloom.bg/3EV9lTl]
((Reuters.Briefs@thomsonreuters.com
)Wonder who could make chips "smarter"?
Jefwilto
Regular
Rise i think he might have been referring to lex555 post
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