Emerging technology for skin cancer detection
Follow the latest news headlines from Australia's most trusted source. Read in-depth expert analysis and watch live coverage on ABC News.
BRN Discussion Ongoing
- Thread starter TechGirl
- Start date
smoothsailing18
Regular
Quack Quack
DingoBorat
Slim
My personal message to our AKD family.............1000, 1.5, 2.0, Pico, M2, PCIe, Edgy Box and within the next 6/8 months AKD... 3.0
will be born.Love U guys
Tech.
Actually sad listening to that song, knowing she died at such a young age..
Such a hauntingly beautiful voice..
I do hate the fact that she has probably help popularise tattoos, metallic zits and overdone makeup and eyelashes..
But eh..
She did have an inherent unconventional natural beauty, underneath all that.
"Some" kind of announcement has to be close now surely
..
Pom down under
Top 20
Shouldnt be long before the shit from the crapper start filling this forum with crap.
I’m missing them
Good Evening Harwig ,Now this is interesting... Possibly us????
Emerging technology for skin cancer detection
Follow the latest news headlines from Australia's most trusted source. Read in-depth expert analysis and watch live coverage on ABC News.www.abc.net.au
Agree , saw this several hour's ago & thought ........ well f%$k me spinning , if our tech could not be integrated in the back end ... image classification / verdict produced instantaneously, alleviating any operator guestimating.
Like minds think alike.
Regards,
Esq.
Tothemoon24
Top 20
Pico is also working on building "specialized chips" for the headset meant to process data from the device's sensors and minimize latency on what is seen in the headset and what is happening in real-time.
ByteDance's Mixed Reality Headset Now Under Development from Pico—Report
ByteDance is developing a mixed reality headset with its subsidiary Pico and making it lightweight is their main focus.
www.techtimes.com
Tothemoon24
Top 20
Apologies if posted ,
June 27, 2025
Modern radar and Radio Frequency (RF) signal processing systems—especially those deployed on platforms like drones, CubeSats, and portable systems—are increasingly limited by strict Size, Weight, Power and Cooling (SWaP-Cool) constraints. These environments demand real-time performance and efficient computation, yet many conventional algorithms are too resource-intensive to operate within such tight margins. As the need for intelligent signal interpretation at the edge grows, it becomes essential to identify processing methods that balance accuracy within these constraints.
One such essential task in radar and RF applications is Automatic Modulation Classification (AMC). AMC enables systems to autonomously recognize the modulation type of incoming signals without prior coordination, a function crucial for dynamic spectrum access, electronic warfare, and cognitive radar systems. However, many existing AI-based AMC models, such as deep CNNs or hybrid ensembles, are computationally heavy and ill-suited for low- SWaP-Cool deployment, creating a pressing gap between performance needs and implementation feasibility.
In this post, we’ll show how BrainChip’s Temporal Event-Based Neural Network (TENN), a state space model, overcomes this challenge. You’ll learn why conventional models fall short in AMC tasks—and how TENN enables efficient, accurate, low-latency classification, even in noisy RF environments.
Unfortunately, traditional deep learning architectures used for AMC come with real drawbacks:
This model was chosen as a reference because it comes from the foundational paper that introduced the RadioML dataset—making it a widely accepted standard for evaluation. As a hybrid of convolutional and LSTM layers, CLDNN offers a meaningful performance baseline by capturing both spectral and temporal features of the input signals in the In-phase (I) and Quadrature (Q) (I/Q) components, which are used to represent complex signals in communication systems.
While more recent models like the Mixture-of-Experts AMC (MoE-AMC) have achieved state-of-the-art accuracy on the RadioML 2018.01A dataset, they rely on complex ensemble strategies involving multiple specialized networks, making them unsuitable for low-SWaP-Cool deployments due to their high computational and memory demands. In contrast, TENN matches or exceeds the accuracy of CLDNN, while operating at a fraction of the resource cost—delivering real-time, low-latency AMC performance with under 4 million MACs and no reliance on using multi-model ensembles or hand-crafted features like spectral pre-processing.
With just ~3.7 million MACs and 276K parameters, TENN is over 100x more efficient than CLDNN, while matching or exceeding its accuracy—even in low-SNR regimes. Moreover, the latency in the table refers to the simulated latency on a A30 GPU for both models.
On the RadioML 2018.01A dataset (24 modulations, –20 to +30 dB), TENN consistently outperforms CLDNN especially in mid to higher SNR scenarios. Here is the performance of TENN compared to CLDNN's over the SNR range of -20 to +30 dB:
By leveraging efficient state-space modeling, TENN delivers:
Schedule a demo with our team to benchmark your modulation use cases on BrainChip’s event-driven AI platform and explore how TENN can be tailored to your RF edge deployment.
semiiphub.com
How to Solve the Size, Weight, Power and Cooling Challenge in Radar & Radio Frequency Modulation Classification
By Aras Pirbadian and Amir NaderiJune 27, 2025
Modern radar and Radio Frequency (RF) signal processing systems—especially those deployed on platforms like drones, CubeSats, and portable systems—are increasingly limited by strict Size, Weight, Power and Cooling (SWaP-Cool) constraints. These environments demand real-time performance and efficient computation, yet many conventional algorithms are too resource-intensive to operate within such tight margins. As the need for intelligent signal interpretation at the edge grows, it becomes essential to identify processing methods that balance accuracy within these constraints.
One such essential task in radar and RF applications is Automatic Modulation Classification (AMC). AMC enables systems to autonomously recognize the modulation type of incoming signals without prior coordination, a function crucial for dynamic spectrum access, electronic warfare, and cognitive radar systems. However, many existing AI-based AMC models, such as deep CNNs or hybrid ensembles, are computationally heavy and ill-suited for low- SWaP-Cool deployment, creating a pressing gap between performance needs and implementation feasibility.
In this post, we’ll show how BrainChip’s Temporal Event-Based Neural Network (TENN), a state space model, overcomes this challenge. You’ll learn why conventional models fall short in AMC tasks—and how TENN enables efficient, accurate, low-latency classification, even in noisy RF environments.
Why Traditional AMC Models Fall Short at the Edge
AMC is essential for identifying unknown or hostile signals, enabling cognitive electronic warfare, and managing spectrum access. But systems like UAVs, edge sensors, and small satellites can’t afford large models that eat power and memory.Unfortunately, traditional deep learning architectures used for AMC come with real drawbacks:
- Hundreds of millions of Multiply Accumulate (MAC) operations resulting in high power consumption and large parameter counts demanding large memory
- Heavy preprocessing requirements (e.g., Fast Fourier Transform (FFTs), spectrograms)
- Still fail to maintain accuracy under 0 dB Signal-to-Noise Ratio (SNR), where signal and noise have similar power.
BrainChip’s TENN Model: A Low-SWaP-Cool Breakthrough for Real-Time RF Signal Processing
BrainChip’s TENN model provides a game-changing alternative. It replaces traditional CNNs with structured state-space layers and is specifically optimized for low SWaP-Cool high-performance RF signal processing. State‑Space Models (SSMs) propagate a compact hidden state forward in time, so they need only constant‑size memory at every step. Modern SSM layers often recast this recurrent update as a convolution of the input with a small set of basis kernels produced by recurrence. Inference‑time efficiency therefore matches that of classic RNNs, but SSMs enjoy a major edge during training: like Transformers, they expose parallelizable convolutional structure, eliminating the strict step‑by‑step back‑propagation bottleneck that slows RNN training. The result is a sequence model that is memory‑frugal in deployment yet markedly faster to train than traditional RNNs, while still capturing long‑range dependencies without the quadratic cost of attention of Transformers.TENN introduces the following innovations:
- A compact state-space modeling that simplifies modulation classification by reducing memory usage and computation—offering a leaner alternative to transformer-based models.
- Tensor contraction optimization, applying efficient strategies to minimize memory footprint, computation and maximize throughput.
- Hybrid SSM architecture that replaces CNN layers and avoids attention mechanisms, maintaining feature richness with lower computational cost.
- Real-time, low-latency inference by eliminating the need for FFTs or buffering at inference time
Matching Accuracy with a Fraction of the Compute
The Convolutional Long Short-Term Deep Neural Network (CLDNN), introduced by O’Shea et al. (2018), was selected as the benchmark model for comparison with BrainChip’s TENN. Although the original RadioML paper did not use the CLDNN acronym, it proposed a hybrid architecture combining convolutional layers with LSTM and fully connected layers—an architecture that has since become widely referred to as CLDNN in the AMC literature.This model was chosen as a reference because it comes from the foundational paper that introduced the RadioML dataset—making it a widely accepted standard for evaluation. As a hybrid of convolutional and LSTM layers, CLDNN offers a meaningful performance baseline by capturing both spectral and temporal features of the input signals in the In-phase (I) and Quadrature (Q) (I/Q) components, which are used to represent complex signals in communication systems.
While more recent models like the Mixture-of-Experts AMC (MoE-AMC) have achieved state-of-the-art accuracy on the RadioML 2018.01A dataset, they rely on complex ensemble strategies involving multiple specialized networks, making them unsuitable for low-SWaP-Cool deployments due to their high computational and memory demands. In contrast, TENN matches or exceeds the accuracy of CLDNN, while operating at a fraction of the resource cost—delivering real-time, low-latency AMC performance with under 4 million MACs and no reliance on using multi-model ensembles or hand-crafted features like spectral pre-processing.
With just ~3.7 million MACs and 276K parameters, TENN is over 100x more efficient than CLDNN, while matching or exceeding its accuracy—even in low-SNR regimes. Moreover, the latency in the table refers to the simulated latency on a A30 GPU for both models.
On the RadioML 2018.01A dataset (24 modulations, –20 to +30 dB), TENN consistently outperforms CLDNN especially in mid to higher SNR scenarios. Here is the performance of TENN compared to CLDNN's over the SNR range of -20 to +30 dB:
Ready to bring low SWaP-Cool AI to your RF platforms?
Today’s RF systems need fast, accurate signal classification that fits into small power and compute envelopes. CLDNN and similar models are simply too resource intensive. With TENN, BrainChip offers a smarter, more scalable approach—one that’s purpose-built for edge intelligence.By leveraging efficient state-space modeling, TENN delivers:
- Dramatically reduces latency, power consumption, and cooling requirements
- Robust accuracy across noisy environments
- Seamless deployment on real-time, mobile RF platforms
Schedule a demo with our team to benchmark your modulation use cases on BrainChip’s event-driven AI platform and explore how TENN can be tailored to your RF edge deployment.
How to Solve the Size, Weight, Power and Cooling Challenge in Radar & Radio Frequency Modulation Classification
In this post, we’ll show how BrainChip’s Temporal Event-Based Neural Network (TENN), a state space model, overcomes this challenge. You’ll learn why...
The analysts who have recently upgraded BrainChip's stock rating from "Sell" to "Hold/Accumulate" include various financial research teams and platforms. Here are some notable mentions:
MegaportX
- MarketBeat: This platform provides insights and ratings on stocks, including BrainChip. They have noted the recent upgrade in sentiment towards BrainChip's stock.
- StockInvest.us: This site has also reported on the upgrade of BrainChip's stock rating, indicating a shift in market perception and potential for future growth.
- Capital.com: Their analysis discusses the stock's performance and market conditions, contributing to the overall understanding of BrainChip's position in the market.
MegaportX
Sorry if already posted, some very interesting comments.
www.linkedin.com
🚀 The Age of Neuromorphic AI: The Next Big Thing Is Here Imagine your smartwatch predicting what you need before you ask. Imagine self-driving cars reacting with human-like instinct. Imagine… | Philip Dodge | 21 comments
🚀 The Age of Neuromorphic AI: The Next Big Thing Is Here Imagine your smartwatch predicting what you need before you ask. Imagine self-driving cars reacting with human-like instinct. Imagine medical devices diagnosing illnesses faster than the best specialists — all while sipping barely any...
OBBB injects $14.9B into drones, autonomy, and attritable tech | Andrew Couillard posted on the topic | LinkedIn
10% of the One Big Beautiful Bill defense increase is going to drones. On July 4th, 2025, the OBBB injected $149.5B into U.S. defense spending and $14.9B of that is targeted at unmanned systems, autonomy, and attritable tech. Here’s the high-level breakdown: 🛸 $3.66B for Unmanned Aerial...
CHIPS
Regular
Sorry if already posted, some very interesting comments.
🚀 The Age of Neuromorphic AI: The Next Big Thing Is Here Imagine your smartwatch predicting what you need before you ask. Imagine self-driving cars reacting with human-like instinct. Imagine… | Philip Dodge | 21 comments
🚀 The Age of Neuromorphic AI: The Next Big Thing Is Here Imagine your smartwatch predicting what you need before you ask. Imagine self-driving cars reacting with human-like instinct. Imagine medical devices diagnosing illnesses faster than the best specialists — all while sipping barely any...
Wow, Philip Dodge is doing a great job promoting BrainChip.
If he is around here, thank you, Philip!
M Anthony Lewis
CTO@BrainChip | AI, Robotics, Disruptive Co..
This seems to be a a significant breakthrough.
Thanks goes to our funding agency Airforce research lab and to the emense guidance we are getting from Raytheon RTX our partner on the airforce award
www.linkedin.com
CTO@BrainChip | AI, Robotics, Disruptive Co..
This seems to be a a significant breakthrough.
Thanks goes to our funding agency Airforce research lab and to the emense guidance we are getting from Raytheon RTX our partner on the airforce award
How to Solve the Size, Weight, Power and Cooling Challenge in Radar & Radio Frequency Modulation Classification | Thomas Hülsing
This is fascinating! Look at the table...!! #neuromorphic #computing https://lnkd.in/eRH8XgwG
Selamlar Dostlar 🙏🏻 Graph Neural Networks ve Spiking Neural Networks gibi düşük güç harcayan nöral tabanlı AI modelleriyle ilgilenirken, neuromorphic işlemcilere merak saldım. Bu işlemciler, insan… | Alican Kiraz
Selamlar Dostlar 🙏🏻 Graph Neural Networks ve Spiking Neural Networks gibi düşük güç harcayan nöral tabanlı AI modelleriyle ilgilenirken, neuromorphic işlemcilere merak saldım. Bu işlemciler, insan beyninin sinir ağlarını taklit ederek olay tabanlı hesaplama yapıyor. Ve yalnızca sensör...
Wow, Philip Dodge is doing a great job promoting BrainChip.
If he is around here, thank you, Philip!
Agree with you, but such a shame that BrainChip (officially) won't do the same!
They are happy for other clients/customers to comments on us, but they seem frozen in fear and unable to promote themselves...
BigDonger101
Founding Member
Trendyol is owned by Alibaba. Quite interesting.View attachment 88542 View attachment 88543 View attachment 88544
Selamlar Dostlar 🙏🏻 Graph Neural Networks ve Spiking Neural Networks gibi düşük güç harcayan nöral tabanlı AI modelleriyle ilgilenirken, neuromorphic işlemcilere merak saldım. Bu işlemciler, insan… | Alican Kiraz
Selamlar Dostlar 🙏🏻 Graph Neural Networks ve Spiking Neural Networks gibi düşük güç harcayan nöral tabanlı AI modelleriyle ilgilenirken, neuromorphic işlemcilere merak saldım. Bu işlemciler, insan beyninin sinir ağlarını taklit ederek olay tabanlı hesaplama yapıyor. Ve yalnızca sensör...
WSJ’s Research & Ratings page, which currently lists one Buy recommendation and no Hold or Sell ratings for BRCHF. I analyst has recently started coverage. There was no buy recommendation 1 month ago. WSJ does not provide names of those who make the recommendations.
Here is the link to the MarketBeat BUY reccomendation.
BrainChip (BRN) Stock Price, News & Analysis
Should You Buy or Sell BrainChip Stock? Get The Latest BRN Stock Analysis, Price Target, Earnings Estimates, and Headlines at MarketBeat.
Great finds MegaportX.
Stock Invest have us as a hold.
stockinvest.us
Stock Invest have us as a hold.
BRN.AX Stock Price Forecast. Should You Buy BRN.AX?
Check if BRN.AX Stock has a Buy or Sell Evaluation. BRN.AX Stock Price (ASX), Forecast, Predictions, Stock Analysis and BrainChip Holdings Limited News.
stockinvest.us
Similar threads
