DingoBorat
Slim
I agree!
You mean this one don't you?..
Still can't understand what she's singing..
But who cares right?...
Last edited:
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
You mean this one don't you?..
Still can't understand what she's singing..
But who cares right?...
Pom down under
Top 20
Looks like the buy side is building so might push towards that or 0.225I'll go with 0.22c finish
Fact Finder
Top 20
Hi All
Just because negative commentary has a stronger effect on brain chemistry than positive or neutral commentary I thought a little reminder was in order given it was posted a little while ago:
Neuromorphic Medical Image Analysis at the Edge: On-Edge training with the Akida Brainchip
E Bråtman, L Dow - 2023 - diva-portal.org… By first creating a convolutional neural network model capable of identifying brain haemorrhage and then moving it onto the neuromorphic processor Akida AKD1000, it allowed the…”
My opinion only DYOR
Fact Finder
Afternoon Sb182 ,
Odd , from the ASX site ..... indicates that on 6th feb , yesterday , 146,350 units were taken short.
Regards,
Esq
7für7
Top 20
Hmm looks more like 0.20 to 0.205 to me but still ok if it holds
Fact Finder
Top 20
Hi All
It has often been said that MegaChips is very quiet which is true but it is also MegaChips modus operandi to be very, very discreet. Try as they might however if you look hard enough in the end they slip up.
It does take a little work though but I found the following paper sometime ago now:
This study applied data augmentation to improve the inference accuracy of edge-artificial intelligence on-chip learning, which uses the fine-tuning technique with limited knowledge and …”
The first thing to note is that these researchers all work for MegaChips.
The second thing is the abstract states:
“This study applied data augmentation to improve the inference accuracy of edge-artificial intelligence on-chip learning, which uses the fine-tuning technique with limited knowledge and without a cloud server. Subsequently, keyword spotting was adopted as an example of the edge-AI application to evaluate inference accuracy. Investigations revealed that all four data augmentation types contributed to inference accuracy improvements, boosting data augmentation by 5.7 times rather than the one-shot boost without data augmentation recorded previously.”
The following link takes you to a sign in page to which I do not have access however whenever this occurs opening the references can sometimes add insight into whether the paper is of interest. In this case it did and I found:
References & Cited By
1.
Akida enablement platforms, [online] Available: https://brainchip.com/akida-enablement-platforms/.
Hide Context Google Scholar
For example, the BrainChip was released as an evaluation chip AKD1000 [1] to execute finetuning with just one-shot input.
Go To Text
2.
M. Horowitz, "computing's energy problem (and what we can do about it)", IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), pp. 10-14, 2014.
Hide Context View Article Google Scholar
Subsequent investigations revealed that it realized a neuromorphic neural network, thereby reducing computational costs [2], [3].
Go To Text
3.
S.I. Ikegawa, R. Saiin, Y. Sawada and N. Natori, "Rethinking the role of normalization and residual blocks for spiking neural networks", Sensors, vol. 22, 2022.
Hide Context CrossRef Google Scholar
Subsequent investigations revealed that it realized a neuromorphic neural network, thereby reducing computational costs [2], [3].
Go To Text
4.
Overview of meta TF, [online] Available: https://doc.brainchipinc.com/index.html.
Hide Context Google Scholar
The Akida neuromorphic ML Framework (MetaTF) [4] was adopted to establish the edge-AI computing environment.
Go To Text
5.
P. Warden, "Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition", arXiv, 2018.
Hide Context Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text
6.
Model zoo performances, [online] Available: https://doc.brainchipinc.com/zoo_performances.html.
Hide Context Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[6][7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text
7.
DS-CNN/KWS inference, [online] Available: https://doc.brainchipinc.com/examples/general/plot_2_ds_cnn_kws.html.
Hide Context Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text
8.
S.B. Davis and P. Mermelstein, "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences", IEEE Transactions on Acoustics Speech, vol. 28, pp. 357-366, 1980.
Hide Context View Article Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text
9.
F. Chollet, Xception: Deep learning with depthwise separable convolutions, 2016, [online] Available: http://arxiv.org/abs/1610.02357.
Hide Context Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text”
Based upon the above it is clear that MegaChip was using AKIDA as the Edge environment and we’re able to show a marked improvement in inference accuracy using four different types of data augmentation for training purposes.
My opinion only DYOR
Fact Finder
It has often been said that MegaChips is very quiet which is true but it is also MegaChips modus operandi to be very, very discreet. Try as they might however if you look hard enough in the end they slip up.
It does take a little work though but I found the following paper sometime ago now:
“Data Augmentation for Edge-AI on-chip Learning
N Yoshida, H Miura, T Matsutani… - 2022 IEEE 8th World …, 2022 - ieeexplore.ieee.orgThis study applied data augmentation to improve the inference accuracy of edge-artificial intelligence on-chip learning, which uses the fine-tuning technique with limited knowledge and …”
The first thing to note is that these researchers all work for MegaChips.
The second thing is the abstract states:
“This study applied data augmentation to improve the inference accuracy of edge-artificial intelligence on-chip learning, which uses the fine-tuning technique with limited knowledge and without a cloud server. Subsequently, keyword spotting was adopted as an example of the edge-AI application to evaluate inference accuracy. Investigations revealed that all four data augmentation types contributed to inference accuracy improvements, boosting data augmentation by 5.7 times rather than the one-shot boost without data augmentation recorded previously.”
The following link takes you to a sign in page to which I do not have access however whenever this occurs opening the references can sometimes add insight into whether the paper is of interest. In this case it did and I found:
References & Cited By
1.
Akida enablement platforms, [online] Available: https://brainchip.com/akida-enablement-platforms/.
Hide Context Google Scholar
For example, the BrainChip was released as an evaluation chip AKD1000 [1] to execute finetuning with just one-shot input.
Go To Text
2.
M. Horowitz, "computing's energy problem (and what we can do about it)", IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), pp. 10-14, 2014.
Hide Context View Article Google Scholar
Subsequent investigations revealed that it realized a neuromorphic neural network, thereby reducing computational costs [2], [3].
Go To Text
3.
S.I. Ikegawa, R. Saiin, Y. Sawada and N. Natori, "Rethinking the role of normalization and residual blocks for spiking neural networks", Sensors, vol. 22, 2022.
Hide Context CrossRef Google Scholar
Subsequent investigations revealed that it realized a neuromorphic neural network, thereby reducing computational costs [2], [3].
Go To Text
4.
Overview of meta TF, [online] Available: https://doc.brainchipinc.com/index.html.
Hide Context Google Scholar
The Akida neuromorphic ML Framework (MetaTF) [4] was adopted to establish the edge-AI computing environment.
Go To Text
5.
P. Warden, "Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition", arXiv, 2018.
Hide Context Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text
6.
Model zoo performances, [online] Available: https://doc.brainchipinc.com/zoo_performances.html.
Hide Context Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[6][7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text
7.
DS-CNN/KWS inference, [online] Available: https://doc.brainchipinc.com/examples/general/plot_2_ds_cnn_kws.html.
Hide Context Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text
8.
S.B. Davis and P. Mermelstein, "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences", IEEE Transactions on Acoustics Speech, vol. 28, pp. 357-366, 1980.
Hide Context View Article Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text
9.
F. Chollet, Xception: Deep learning with depthwise separable convolutions, 2016, [online] Available: http://arxiv.org/abs/1610.02357.
Hide Context Google Scholar
Subsequently, the google speech command dataset was applied to train KWS [5]–[7], after which audio files were transformed to a Mel-frequency power spectrogram [8], thereby aiding supply to DS-CNN [9].
Go To Text”
Based upon the above it is clear that MegaChip was using AKIDA as the Edge environment and we’re able to show a marked improvement in inference accuracy using four different types of data augmentation for training purposes.
My opinion only DYOR
Fact Finder
.
Worker122
Regular
Solid work FF, thanks
buena suerte :-)
BOB Bank of Brainchip
David holland
Regular
These 2 IP Deals products definitely should be hitting the market
cosors
👀
Good morning!
I let myself be lured (?) out of my reserve yesterday. After sleeping on it for a night, I've come to the conclusion that it was unnecessary. The only correct way to deal with these types of people is to ignore them. Attention is their stage. If everyone turned away, they would have lost completely. They want attention to get our money. So I have deleted this post of my own accord.
cosors
👀
Pom down under
Top 20
Cisco and NVIDIA to Help Enterprises Quickly and Easily Deploy and Manage Secure AI Infrastructure
CISCO LIVE EMEA -- Cisco and NVIDIA today announced plans to deliver AI infrastructure solutions for the data center that are easy to deploy and manage, enabling the massive computing power that enterprises need to succeed in the AI era.
sg.finance.yahoo.com
Pom down under
Top 20
It would have been rude not too spend your last $$$ on a few more shares. Every little counts 
Afternoon Chippers ,
After several beers & much thought....
Thinking the German bourse may have another cracker day followed by us ( largest floating penile colony on earth government by asx , asic & general big Dicks domiciled offshore ), also known as Australia.
Something feels afoot as is evident in last two days fun and games.
May be in for a little of this...
Purely my thoughts .
Regards,
Esq
After several beers & much thought....
Thinking the German bourse may have another cracker day followed by us ( largest floating penile colony on earth government by asx , asic & general big Dicks domiciled offshore ), also known as Australia.
Something feels afoot as is evident in last two days fun and games.
May be in for a little of this...
Purely my thoughts .
Regards,
Esq
Stable Genius
Regular
Hi all,
A long read but in my opinion worth it.
Here’s an excerpt;
This is in large part due to significant growth at the edge and telecom edge, as AI capabilities are harnessed closer to the end-user. In terms of industry vertical, IT & Telecoms is expected to lead the way for AI chip usage over the next decade, with Banking, Financial Services & Insurance (BFSI) close behind, and Consumer Electronics behind that. Of these, the Consumer Electronics industry vertical is to generate the most revenue at the edge, given the further rollout of AI into consumer products for the home. More information regarding industry vertical breakout can be found in the relevant AI reports.
AI chips revenue segmented by purpose.
Revenue generated by AI chips is set to grow at a CAGR of 22% over the next ten years, up to 2034. Of the revenue generated at this time, AI chips for inference purposes will dominate over those used for AI training, as AI migrates more thoroughly to deployment at the edge of the network.
For more information regarding key trends and market segmentations with regards AI chips over the next ten years, please refer to the two reports: “AI Chips: 2023-2033” and “AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge“.
www.edge-ai-vision.com
Enjoy!
A long read but in my opinion worth it.
Here’s an excerpt;
Growth of AI chips over the next decade
Revenue generated from the sale of AI chips (including the sale of physical chips and the rental of chips via cloud services) is expected to rise to just shy of USD$300 billion by 2034, at a compound annual growth rate of 22% from 2024 to 2034. This revenue figure incorporates the use of chips for the acceleration of machine learning workloads at the edge of the network, for telecom edge, and within data centers in the cloud. As of 2024, chips for inference purposes (both at the edge and within the cloud) comprise 63% of revenue generated, with this share growing to more than two-thirds of the total revenue by 2034.This is in large part due to significant growth at the edge and telecom edge, as AI capabilities are harnessed closer to the end-user. In terms of industry vertical, IT & Telecoms is expected to lead the way for AI chip usage over the next decade, with Banking, Financial Services & Insurance (BFSI) close behind, and Consumer Electronics behind that. Of these, the Consumer Electronics industry vertical is to generate the most revenue at the edge, given the further rollout of AI into consumer products for the home. More information regarding industry vertical breakout can be found in the relevant AI reports.
AI chips revenue segmented by purpose.
Revenue generated by AI chips is set to grow at a CAGR of 22% over the next ten years, up to 2034. Of the revenue generated at this time, AI chips for inference purposes will dominate over those used for AI training, as AI migrates more thoroughly to deployment at the edge of the network.
For more information regarding key trends and market segmentations with regards AI chips over the next ten years, please refer to the two reports: “AI Chips: 2023-2033” and “AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge“.
The Age of Artificial Intelligence: AI Chips to 2034
Artificial Intelligence is transforming the world as we know it; from the success of DeepMind over Go world champion Lee Sedol in 2016 to the robust
www.edge-ai-vision.com
Enjoy!
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