F
Filobeddo
Guest
or maybe Nearenough??View attachment 8370
Interestingly RT likes but no comment belowI guess surely they know each other with 22yrs behind him, and surely there is no doubting he was ever Goodenough !!
BRN Discussion Ongoing
- Thread starter TechGirl
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Just a word of warning about moving into the ASX200 for when it occurs.
Those institutions that have to buy the ASX200 also lend shares for shorting.
Thus larger more financially literate shorters become involved.
This then attracts larger more sophisticated traders who know what the shorters are going to do and follow their lead.
In the early days of the spice trade in England and Europe sailing ships would set sail to Asia for the purpose of trading to bring back these valuable commodities. The journey was long and very dangerous yet for those who embarked on the journey and survived the roaring forties rounding the cape and privateer attacks the riches which flowed on a successful return were extraordinary.
In many ways the investment journey on the ASX apart from the risk to life is similar, though an angry spouse might pose a physical threat, at every turn there are new bigger obstacles as you get closer to the golden ring.
The manipulators will be more articulate and subtle. Suggesting risks of different types and feigning friendship. The trades and shorts will come in bigger more sophisticated waves.
Just like those involved in the spice trade those who stay the course, ignore the distractions and complete the journey will be rewarded.
Abandoning ship guarantees failure.
My opinion only DYOR
FF
AKIDA BALLISTA
Those institutions that have to buy the ASX200 also lend shares for shorting.
Thus larger more financially literate shorters become involved.
This then attracts larger more sophisticated traders who know what the shorters are going to do and follow their lead.
In the early days of the spice trade in England and Europe sailing ships would set sail to Asia for the purpose of trading to bring back these valuable commodities. The journey was long and very dangerous yet for those who embarked on the journey and survived the roaring forties rounding the cape and privateer attacks the riches which flowed on a successful return were extraordinary.
In many ways the investment journey on the ASX apart from the risk to life is similar, though an angry spouse might pose a physical threat, at every turn there are new bigger obstacles as you get closer to the golden ring.
The manipulators will be more articulate and subtle. Suggesting risks of different types and feigning friendship. The trades and shorts will come in bigger more sophisticated waves.
Just like those involved in the spice trade those who stay the course, ignore the distractions and complete the journey will be rewarded.
Abandoning ship guarantees failure.
My opinion only DYOR
FF
AKIDA BALLISTA
D
Deleted member 118
Guest
BaconLover
Founding Member
Yes.
Case in point - inclusion of BRN into ASX300. I didn't buy straight in on my Super because there was too much noise. Instos tend to make use of it, trading it both ways for those who remember.
@dippY22 @Beebo US tech stocks are taking a bashing ATM, so that possibly explains the lack of interest for ADR right now.
talking about Eco system not only Tech but the Brian behind all the Tech are also moving between Company.
www.edgeimpulse.com
Big News! Edge Impulse Announces Simon Segars Joining the Board of Directors
Simon Segars, the distinguished technology leader and former CEO of Arm, brings deep industry experience and leadership as he joins our board.
www.edgeimpulse.com
Learning
Learning to the Top 🕵♂️
I haven't seen this been discussed on TSEX, not sure about the old place.
BOSCH Center for Artificial Intelligence has been studying SNN for years
www.frontiersin.org
www.frontiersin.org
www.frontiersin.org
Can some of our more learned members draw inference from the above studies from BOSCH.
Its great to be a shareholder
Learning.
BOSCH Center for Artificial Intelligence has been studying SNN for years
Comparison of Artificial and Spiking Neural Networks on Digital Hardware
Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an unsustainable rate in the push for greater accuracy...
Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks
Spiking neural networks (SNNs) are potentially highly efficient models for inference on fully parallel neuromorphic hardware, but existing training methods that convert conventional artificial neural networks (ANNs) into SNNs are unable to exploit these advantages. Although ANN-to-SNN conversion...
Deep Learning With Spiking Neurons: Opportunities and Challenges
Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference...
Can some of our more learned members draw inference from the above studies from BOSCH.
Its great to be a shareholder
Learning.
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Fact Finder
Top 20
HC the Bermuda Triangle of the investment world best to steer a course around that obstacle lest the siren call will rob you of your sanity.
Here too the waters will become less clear and reefs will be obscured and new participants with friendly countenance and intriguingly seductive questions which do not matter at all will ask the what if’s that subtly erode confidence.
They will claim experience that allows them to provide answers to questions they raise and the answers will be negative or designed to erode confidence.
They will throw up research and claim it points to being on the wrong course all the time ignoring the achievements of the company.
They will never however answer the single most important question which is “why will Brainchip not achieve a tiny little one percent of the world wide addressable market for Edge Ai.”
ARM alone caused 29 billion semiconductors to be shipped in the last 12 months.
One percent of this is 290 million and at a 10 cent royalty per semiconductor that is $29 million which is north of twice Brainchip’s spend making it a profitable company.
Until someone can tell us why Brainchip will not achieve one percent of the World wide market for Edge Ai said to be 70 billion US dollars by 2025 when they already are trusted by:
NASA
MERCEDES BENZ
VALEO
MEGACHIPS
RENESAS
ARM
EDGE IMPULSE and
SiFIVE
they are no more than white noise.
By the way one percent of 70 billion US dollars is 700 million US dollars.
Annual outgoings at Brainchip are about $12 million so even if we bump that up to $50 million that is still $650 million in profit.
My opinion only DYOR
FF
AKIDA BALLISTA
Kachoo
Regular
I'm an ADR buyer the volume is and has always been low. I bought them cause these shares are not shortable so I thought for a long term view they are the best for me. I also have asx shares and yeah they are liquid like mad hence the volitility. I bought the ADR not for trades but a 5 to 10 year picture when they are finally in demand you will see action but at that time it will be listed on the NASDQ and well north of today's prices.
Seagars joining the board of directors for Edge Impulse is Huge news for BrainChip.
“Unlocking our world's potential through smarter, more efficient, and accessible computing has been a consistent theme in my professional journey,” Simon says. “Edge Impulse shares this vision and is at the center of the next frontier of edge computing, by empowering developers to harness machine learning across all classes of edge devices.”
Shadow59
Regular
M_C
Founding Member
I wonder who's board of directors he may also join........................................wouldn't rule it out considering the large portion of former ARM employees we already have on the books.
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buena suerte :-)
BOB Bank of Brainchip
HELLS CORNERWhat is HC anyway?
Fact Finder
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Hi @LearningI haven't seen this been discussed on TSEX, not sure about the old place.
BOSCH Center for Artificial Intelligence has been studying SNN for years
Comparison of Artificial and Spiking Neural Networks on Digital Hardware
Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an unsustainable rate in the push for greater accuracy...Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks
Spiking neural networks (SNNs) are potentially highly efficient models for inference on fully parallel neuromorphic hardware, but existing training methods that convert conventional artificial neural networks (ANNs) into SNNs are unable to exploit these advantages. Although ANN-to-SNN conversion...Deep Learning With Spiking Neurons: Opportunities and Challenges
Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference...
Can some of our more learned members draw inference from the above studies from BOSCH.
Its great to be a shareholder
Learning.
We spent a lot of time and energy over on HC looking at Bosch and @uiux is a Bosch savant.
The bottom line is they have been trying to play catch-up and unfortunately for them Peter van der Made has been there and done that and blown up the bridges behind him.
The following extract sets out the problem they are yet to resolve
“Adaptively rescaling the SNN weights or firing thresholds could be a solution to alleviate this effect. This can be seen as a kind of homeostasis mechanism that keeps the overall firing rates of SNNs at a constant level.”
But this problem is already dealt with and the solution patented by Peter van der Made so if they eventually get passed this point in their research and seek a patent then likely will suffer pushback.
Despite Brainchip holding a patent for CNN to SNN conversion you will notice there is not one reference to Brainchip in this paper which is rather telling as they are clearly not confident they have found a more efficient work around.
In fact they are still a long way from a solution as the above extract makes clear and as they state in their concluding paragraph more research is required.
They also state in their paper that they are yet to identify how to create the hardware upon which to run their converter which also has been done and patented by Peter van der Made and team.
My opinion only DYOR
FF
AKIDA BALLISTA
Fact Finder
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I should add that while Bosch is a truly great company last year they sat at number 6 in a list of global providers of semiconductors to the automotive industry and Renesas was at number 3.
Once again Renesas who also had an Ai Edge research program jumped on board the Brainchip Bus and the one percent question raises its ugly head.
My opinion only DYOR
FF
AKIDA BALLISTA
Boab
I wish I could paint like Vincent
Learning
Learning to the Top 🕵♂️
ndefries
Regular
Makes me wonder why they bother trying. Just bolt on some Akida IP and forge ahead.
Fact Finder
Top 20
These researchers have found another use for an SNN Chip:
“1 Introduction
Cloud services cause an exponentially growing traffic in data centers. This requires optical transceivers to operate at lower power and lower cost. Because digital signal processing (DSP) has high power consumption, recent research envisions replacing the DSP in part with an analog frontend with lower power
consumption….
“Conclusions
In this work, we have implemented an SNN on the mixed-signal neuromorphic hardware system BrainScaleS-2 (BSS-2). While we observed a hardware penalty slightly below 1dB at a BER of 2×10−3, the BSS-2 SNN outperforms a simulated linear equalizer with the same number of taps, thanks to nonlinear processing.
The presented results confirm that neuromorphic hardware can provide the reliability required by signal processing in optical transceivers.
Promising directions
for future research include reducing the hardware penalty, reducing the architec- tural complexity and increasing the intrin- sic speed of the SNN, and optimizing the input and output interfaces of the SNN. Furthermore, processing by the SNN must be parallelized. Also, the effective power consumption of neuromorphic signal pro- cessing should be analyzed and compared to digital processing.
In a future hardware implementation, spikes could be generated directly from the electrical signal coming from the photo diode, thereby replacing the power-hungry analog-to-digital converter (ADC). In this work, we have considered hard decision demapping. Another interesting direction is the design of an SNN equalizer/demap- per with soft output.”
About Brainscale:
4 BrainScaleS-2 System
We deploy our SNN equalizer/demapper on the accelerated mixed-signal neuromor phic BSS-2 system, developed at Heidel- berg University [5] in Germany.
On its analog neural network core, it emulates up to 512 LIF neurons and 128 k synapses in analog circuits in parallel and in continu- ous time. Hardware synapses have 6-bit weights, which can be configured as in- hibitory (negative sign) or excitatory (pos- itive sign). The neurons communicate via digital spike events. Each neuron is parameterized individually.
My opinion only DYOR
FF
AKIDA BALLISTA
“1 Introduction
Cloud services cause an exponentially growing traffic in data centers. This requires optical transceivers to operate at lower power and lower cost. Because digital signal processing (DSP) has high power consumption, recent research envisions replacing the DSP in part with an analog frontend with lower power
consumption….
“Conclusions
In this work, we have implemented an SNN on the mixed-signal neuromorphic hardware system BrainScaleS-2 (BSS-2). While we observed a hardware penalty slightly below 1dB at a BER of 2×10−3, the BSS-2 SNN outperforms a simulated linear equalizer with the same number of taps, thanks to nonlinear processing.
The presented results confirm that neuromorphic hardware can provide the reliability required by signal processing in optical transceivers.
Promising directions
for future research include reducing the hardware penalty, reducing the architec- tural complexity and increasing the intrin- sic speed of the SNN, and optimizing the input and output interfaces of the SNN. Furthermore, processing by the SNN must be parallelized. Also, the effective power consumption of neuromorphic signal pro- cessing should be analyzed and compared to digital processing.
In a future hardware implementation, spikes could be generated directly from the electrical signal coming from the photo diode, thereby replacing the power-hungry analog-to-digital converter (ADC). In this work, we have considered hard decision demapping. Another interesting direction is the design of an SNN equalizer/demap- per with soft output.”
About Brainscale:
4 BrainScaleS-2 System
We deploy our SNN equalizer/demapper on the accelerated mixed-signal neuromor phic BSS-2 system, developed at Heidel- berg University [5] in Germany.
On its analog neural network core, it emulates up to 512 LIF neurons and 128 k synapses in analog circuits in parallel and in continu- ous time. Hardware synapses have 6-bit weights, which can be configured as in- hibitory (negative sign) or excitatory (pos- itive sign). The neurons communicate via digital spike events. Each neuron is parameterized individually.
My opinion only DYOR
FF
AKIDA BALLISTA
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