BRN Discussion Ongoing

[White Horse]

Are you kidding me Larry? ..

Rachel Maddow?? The Extreme Far Left Democrats apologist, who MSNBC (known as MSDNC) for some reason, seem to think is worth paying a salary of 25 million USD a year? (she recently took a USD 5 million cut, for poor ratings though, it was 30..).

Yes Fox News is Right leaning, but my clip wasn't an "opinion piece" from Left leaning propagandists, it was directly from the Trump administration.

You've got Elon Musk there, who is one of the smartest people on the Planet and of our Time, who strives for Humanity's Future, trying to help actually shrink government and weed out Waste, Fraud and Abuse.
And the Democrats, are screaming about it??..
Their supporters are fire bombing and vandalising Teslas??
They say almost nothing against the violence??..

Do you have any idea, how many high level Democrat politicians, are multi multi millionaires??
They only get around 170k a year!

All the fuss and screaming by them and the wasteful government employees and bureaucrats, is because they don't want to be taken off the cash cow, that all the Waste, Fraud and Abuse provides them.

They are like blood sucking ticks and parasites, that don't want to be taken off their host, even though the host is on multiple blood transfusions, just to stay alive.

The Democratic party of the US is corrupt and they sicken me.

They are actually opposing the bringing in of Voter ID and registration/proof of citizenship??
I wonder if that has anything to to with the fact, that the only states they won, didn't require any and were flooded with illegal immigrants??..

CNN and MSNBC are their mouth pieces.

Australia, is virtually a "Blue State" of America, it's mainstream media, is for all intents and purposes, controlled by the same interests, as CNN and MSNBC.

Drastic changes are needed and there is a 2 day plunge on the overvalued US markets, driven by extreme fear and you've got a virtually retarded and borderline alcoholic Kamala Harris, coming out cackling and saying "I told you so?"..

Please, give me a break.

You poor brainwashed fool.

I laugh at the yanks. They have become one of the most self destructive countries on earth.
They do not understand that, you reap what you sow.
They are blaming other countries for something they have contrived all by themselves.
Their big business has been taking manufacturing of shore for years. One of the most recent, their favorite son, Elon Musk.
There biggest companies have using Ireland and other tax havens for years.
You could be forgiven for thinking that this whole thing is a scam, to make the rich, richer and the poor penniless.
Here is a short list from chat GPT of tax manipulators.
Question;- Which US companies use Ireland as a tax haven.
Several large U.S. multinational corporations have historically used Ireland as a tax haven due to its low corporate tax rate (12.5%) and favorable tax laws. These companies typically establish subsidiaries or headquarters in Ireland to reduce their global tax liabilities through strategies like the “Double Irish” and transfer pricing.

Notable U.S. Companies That Have Used Ireland as a Tax Haven:​

1. Apple
   • One of the most prominent examples.
   • Used Irish subsidiaries to hold intellectual property rights and shift profits.
   • At one point paid an effective tax rate of less than 1%.
   • Was ordered by the EU in 2016 to pay €13 billion in back taxes to Ireland (which Ireland actually contested).
2. Google (Alphabet Inc.)
   • Used the “Double Irish, Dutch Sandwich” strategy to route profits through Irish and Dutch subsidiaries to Bermuda.
   • Saved billions in taxes before the loophole was closed to new arrangements in 2015 (phased out by 2020).
3. Facebook (Meta)
   • Has routed much of its non-U.S. revenue through its Irish operations.
   • Took advantage of Ireland’s lower tax rate and IP-friendly laws.
4. Microsoft
   • Has extensive operations in Ireland, with much of its international profits funneled through subsidiaries there.
   • In 2020, one Irish subsidiary reported $315 billion in profits but paid zero corporate tax because it was “resident” in Bermuda for tax purposes.
5. Pfizer
   • Attempted a tax inversion deal by merging with Irish-based Allergan in 2016 (ultimately blocked by U.S. authorities).
   • Still benefits from Irish tax laws through various subsidiaries.
6. Amazon
   • Uses Irish operations for EU sales, and has been scrutinized for aggressive tax minimization strategies.
7. Facebook, LinkedIn, and Twitter(before Twitter was acquired by Elon Musk)
   • All had European headquarters in Dublin and used Ireland to manage non-U.S. revenues for tax purposes.

Why Ireland?​

• Low corporate tax rate (12.5%)
• Favorable IP laws
• EU single market access
• Skilled, English-speaking workforce
• Legal structures that allowed aggressive tax planning

 

[DingoBorat]

wow what a lot of falsehoods you have stated. 70% of billionaire contributors were for the Republicans (https://www.theguardian.com/us-news/2025/apr/01/billionaires-record-spending-2024-election)
The majority of Americans do not support Trump, he got 49.8 % of turnout votes so just in that statistic alone 50.2% didn't vote for him and secondly 264,800,000 approx were eligible to vote 2024 and only 156,303, 000 approx turned out,( https://en.wikipedia.org/wiki/Voter_turnout_in_United_States_presidential_elections) Trump only got 77,303,573, a far cry from the having the majority supporting Trump.
Go Brainchip, hoping the company survives this mess being created in the USA

Kamala Harris Has More Billionaires Prominently Backing Her Than Trump—Bezos And Griffin Weigh In (Updated)

"Even though I have plenty of disagreements with their platform...I am voting for Kamala Harris and Tim Walz," Schwarzenegger, the former Republican governor of California, wrote Wednesday.

www.forbes.com

I don't really know where Forbes stand, but The Guardian, is definitely a Left leaning media source.

Trump won both the Electoral and Popular vote, look it up.

Popular vote, is the majority of people who voted.

The US, has the Democratic right, not to vote.

Stating the percentage of people who voted, against those "eligible" to vote, simply doesn't make any sense.

 

[IloveLamp]

Omg can we make a Trump thread please and keep the discussion thread for brn?
View attachment 81549

Or not, you know......whatever

 

[DingoBorat]

You poor brainwashed fool.

I laugh at the yanks. They have become one of the most self destructive countries on earth.
They do not understand that, you reap what you sow.
They blaming other countries for something they have contrived all by themselves.
Their big business has been taking manufacturing of shore for years. One of the most recent, their favorite son, Elon Musk.
There biggest companies have using Ireland and other tax havens for years.
You could be forgiven for thinking that this whole thing is a scam, to make the rich, richer and the poor penniless.
Here is a short list from chat GPT of tax manipulators.
Question;- Which US companies use Ireland as a tax haven.
Several large U.S. multinational corporations have historically used Ireland as a tax haven due to its low corporate tax rate (12.5%) and favorable tax laws. These companies typically establish subsidiaries or headquarters in Ireland to reduce their global tax liabilities through strategies like the “Double Irish” and transfer pricing.

Notable U.S. Companies That Have Used Ireland as a Tax Haven:​

1. Apple
   • One of the most prominent examples.
   • Used Irish subsidiaries to hold intellectual property rights and shift profits.
   • At one point paid an effective tax rate of less than 1%.
   • Was ordered by the EU in 2016 to pay €13 billion in back taxes to Ireland (which Ireland actually contested).
2. Google (Alphabet Inc.)
   • Used the “Double Irish, Dutch Sandwich” strategy to route profits through Irish and Dutch subsidiaries to Bermuda.
   • Saved billions in taxes before the loophole was closed to new arrangements in 2015 (phased out by 2020).
3. Facebook (Meta)
   • Has routed much of its non-U.S. revenue through its Irish operations.
   • Took advantage of Ireland’s lower tax rate and IP-friendly laws.
4. Microsoft
   • Has extensive operations in Ireland, with much of its international profits funneled through subsidiaries there.
   • In 2020, one Irish subsidiary reported $315 billion in profits but paid zero corporate tax because it was “resident” in Bermuda for tax purposes.
5. Pfizer
   • Attempted a tax inversion deal by merging with Irish-based Allergan in 2016 (ultimately blocked by U.S. authorities).
   • Still benefits from Irish tax laws through various subsidiaries.
6. Amazon
   • Uses Irish operations for EU sales, and has been scrutinized for aggressive tax minimization strategies.
7. Facebook, LinkedIn, and Twitter (before Twitter was acquired by Elon Musk)
   • All had European headquarters in Dublin and used Ireland to manage non-U.S. revenues for tax purposes.

Why Ireland?​

• Low corporate tax rate (12.5%)
• Favorable IP laws
• EU single market access
• Skilled, English-speaking workforce
• Legal structures that allowed aggressive tax planning

Not sure why you are calling me a poor brainwashed fool.

I agree with this statement and it's what Trump is fixing.

"I laugh at the yanks. They have become one of the most self destructive countries on earth.
They do not understand that, you reap what you sow.
They blaming other countries for something they have contrived all by themselves.
Their big business has been taking manufacturing of shore for years. One of the most recent, their favorite son, Elon Musk.
There biggest companies have using Ireland and other tax havens for years.
You could be forgiven for thinking that this whole thing is a scam, to make the rich, richer and the poor penniless."

I have hated the US and what they had become, until this recent turn around.
I blame the US, for a lot of things that have happened in the past and the crimes against humanity, that they have committed.

But you are free to think of me as brainwashed, if you wish.

 

[DingoBorat]

AKIDA TREBUCHET!

 

[7für7]

All people are, in some way, brainwashed. It’s really inappropriate and arrogant to believe you’re on the “right” side without any influence. Personally, I’ve just noticed that, strangely enough, it’s usually those who lean to the left who become immediately insulting and mocking when you present counterarguments. I’m someone who supports center-right parties. I openly admit it. The world has drifted so far to the left that people no longer recognize the dangers of socialism in the name of democracy—while labeling everything else as “Nazi.”

If people knew me and my family, they’d think twice before calling me a Nazi—especially considering the word “socialism” is part of that term, which fundamentally cannot be linked to the right wing. So I wonder who’s really been manipulated for years to confuse these connections. But you won’t get anywhere with political views here anyway! So let’s just leave it… everyone should vote for whoever they want. The majority wins. That’s democracy. And if someone doesn’t like that, they’re welcome to move to North Korea, Sudan, or the Congo.

 

[7für7]

Maybe already posted?

The Rise Of Generative AI On The Edge

Deploying GenAI in applications where real-time processing, privacy, and security are paramount.

semiengineering.com

 

[7für7]

Or this?

Edge AI could test Starlink's mettle

AI workloads are expected to move from centralized training to inferencing at the edgeStarlink might have more trouble serving these workloads than terrestria | Could capacity and latency issues hinder Starlink's consumer AI ambitions?

www.fierce-network.com

 

[TheDrooben]

Since DB had one last post (again) I will throw this out as my last post on the Trump/Musk love in......

https://www.politico.com/news/2025/04/05/elon-musk-peter-navarro-tariffs-00006930

 

[Food4 thought]

I am interested to know if anyone picked up on who is selling these doorbells they speak of, any idea anyone ?.

Quandary
Doorbells : is a code name for let’s fuck with peoples money and minds
It’s been used before by some other companies
Keeping everyone in the dark
It sounds innocent, most people have one

But who will the doorbell let in to the inner circle of the truth. ??

Just be prepared

They are coming for you

 

[Food4 thought]

Tomorrow is one day closer to getting an announcement.

But what will the announcement be?

Another partnership
A sale of 150,000 dollars of chips
Or a fricken big contract that will get us going
I feel like we are always the bridesmaid but never the bride.
BrainChip must be sick of kissing frogs where is our prince?

It must be close
Deal of the century
Sean make it rain.

 

[CHIPS]

Snapdragon 8 gen 4 versus AKIDA.

Question to ChatGPT:
Can Snapdragon handle 1 billion parameters on device unconnected for GENAi applications on a watch battery?


Answer:
"If your GenAI workload must run completely untethered on a watch battery (and for more than a few minutes), Snapdragon is not the right fit. Akida or neuromorphic architectures are the way forward."



View attachment 81548

I asked GROK on X: Please compare snapdragon with akida and who is the winner?

To compare Snapdragon (developed by Qualcomm) with Akida (developed by BrainChip) for on-device AI applications, we need to evaluate their architectures, performance, power efficiency, and suitability for generative AI (GenAI) tasks, such as running a 1-billion-parameter model on a resource-constrained device. Both platforms target edge AI, but they differ significantly in design philosophy, hardware, and use cases. Let’s break it down.

Snapdragon Overview
Snapdragon processors, particularly high-end models like the Snapdragon 8 Gen 3 or Wear series for wearables, are system-on-chip (SoC) solutions widely used in smartphones, wearables, and IoT devices. They feature a heterogeneous computing architecture with a CPU (Kryo), GPU (Adreno), DSP (Hexagon), and a dedicated Neural Processing Unit (NPU). The Snapdragon 8 Gen 3, for example, delivers up to 45 TOPS (trillion operations per second) of AI performance, supports models with up to 10 billion parameters, and has demonstrated on-device Stable Diffusion (1 billion parameters) in under a second on a smartphone. Qualcomm emphasizes power efficiency through techniques like quantization (e.g., INT4/INT8) and has optimized its NPU for low-latency inference. For wearables, Snapdragon Wear chips (e.g., Wear 5100) are less powerful, offering sub-1 TOPS and limited memory (1–2 GB RAM), tailored for basic tasks like fitness tracking rather than heavy AI workloads.

Akida Overview
Akida, from BrainChip, is a specialized neuromorphic processor designed to mimic the human brain’s spiking neural network (SNN) architecture. Unlike traditional processors, Akida uses event-based processing, where computations occur only when triggered by input spikes, drastically reducing power consumption for sparse, real-time data. The Akida AKD1000 chip, for instance, integrates with an SoC like NXP’s i.MX 8M Plus in products like the Akida Edge AI Box. It excels at tasks like vision, voice, and sensor processing, with a focus on low-power edge applications. BrainChip claims Akida can handle convolutional neural networks (CNNs) converted to SNNs, achieving high efficiency (e.g., 1–2 watts for inference), but its parameter capacity is less clear—likely in the millions to low billions when paired with a host SoC.
Comparison

1. Architecture and Compute Power

• Snapdragon: A general-purpose SoC with a powerful NPU, offering 45 TOPS (8 Gen 3) or sub-1 TOPS (Wear series). It’s optimized for dense matrix operations in deep neural networks (DNNs) and supports a wide range of AI models, including large language models (LLMs) and generative AI.
• Akida: A neuromorphic chip focused on SNNs, with lower raw compute (exact TOPS unspecified but likely <10 TOPS even with a host SoC). It’s specialized for sparse, event-driven tasks rather than general-purpose AI.
• Winner: Snapdragon for raw compute power and versatility, especially for GenAI tasks requiring billions of parameters.

2. Power Efficiency

• Snapdragon: Highly efficient for its class, with the 8 Gen 3 achieving ~0.5 watts per inference for a 1-billion-parameter model (hypothetical, based on optimizations). Wear chips are even lower power but lack the capacity for large models.
• Akida: Potentially more efficient for specific workloads due to its event-based design—consuming microjoules per spike event. For a 1-billion-parameter SNN, power could be <1 watt, but this assumes the model fits its architecture.
• Winner: Akida for ultra-low-power scenarios; Snapdragon for balanced efficiency at scale.

3. Model Capacity and GenAI Suitability

• Snapdragon: Proven to handle 1-billion-parameter models (e.g., Stable Diffusion) and up to 10 billion on high-end chips. Its memory bandwidth and NPU make it ideal for GenAI on-device.
• Akida: Limited by its neuromorphic design and reliance on a host SoC. While it can process CNNs and smaller SNNs, scaling to 1 billion parameters is unproven and likely constrained by memory (e.g., 500 MB for weights alone exceeds typical edge device RAM).
• Winner: Snapdragon, as it’s explicitly built for large-scale GenAI.

4. Use Case Fit (Watch Battery Context)

• Snapdragon: On a watch battery (e.g., 300 mAh, 1.11 Wh), a high-end Snapdragon could run thousands of inferences sporadically, but current Wear chips can’t handle 1 billion parameters due to compute and memory limits.
• Akida: Designed for edge devices, it could theoretically operate on a watch battery for lightweight SNN tasks (e.g., gesture recognition), but not for GenAI at 1 billion parameters without significant host support.
• Winner: Neither fully wins here—Snapdragon Wear falls short, and Akida isn’t suited for GenAI at this scale.

Who Wins?

• For GenAI with 1 Billion Parameters on a Watch Battery: Snapdragon is the winner. Its high-end chips (e.g., 8 Gen 3) have demonstrated this capability on smartphones, and future Wear variants could scale down with optimization. Akida’s neuromorphic approach, while innovative, lacks the proven capacity and ecosystem for such large models.
• 
  
• Overall Edge AI: Snapdragon dominates due to its versatility, ecosystem (Qualcomm AI Hub), and widespread adoption. Akida shines in niche, ultra-low-power applications like sensor processing but isn’t a direct competitor for GenAI workloads.

In short, Snapdragon wins for the specific scenario of handling 1 billion parameters for GenAI on-device, even under watch battery constraints, assuming future wearable-optimized versions. Akida is a compelling alternative for smaller, event-driven AI tasks but doesn’t match Snapdragon’s breadth or power for this use case.

 

[Deadpool]

I asked GROK on X: Please compare snapdragon with akida and who is the winner?

To compare Snapdragon (developed by Qualcomm) with Akida (developed by BrainChip) for on-device AI applications, we need to evaluate their architectures, performance, power efficiency, and suitability for generative AI (GenAI) tasks, such as running a 1-billion-parameter model on a resource-constrained device. Both platforms target edge AI, but they differ significantly in design philosophy, hardware, and use cases. Let’s break it down.

Snapdragon Overview
Snapdragon processors, particularly high-end models like the Snapdragon 8 Gen 3 or Wear series for wearables, are system-on-chip (SoC) solutions widely used in smartphones, wearables, and IoT devices. They feature a heterogeneous computing architecture with a CPU (Kryo), GPU (Adreno), DSP (Hexagon), and a dedicated Neural Processing Unit (NPU). The Snapdragon 8 Gen 3, for example, delivers up to 45 TOPS (trillion operations per second) of AI performance, supports models with up to 10 billion parameters, and has demonstrated on-device Stable Diffusion (1 billion parameters) in under a second on a smartphone. Qualcomm emphasizes power efficiency through techniques like quantization (e.g., INT4/INT8) and has optimized its NPU for low-latency inference. For wearables, Snapdragon Wear chips (e.g., Wear 5100) are less powerful, offering sub-1 TOPS and limited memory (1–2 GB RAM), tailored for basic tasks like fitness tracking rather than heavy AI workloads.

Akida Overview
Akida, from BrainChip, is a specialized neuromorphic processor designed to mimic the human brain’s spiking neural network (SNN) architecture. Unlike traditional processors, Akida uses event-based processing, where computations occur only when triggered by input spikes, drastically reducing power consumption for sparse, real-time data. The Akida AKD1000 chip, for instance, integrates with an SoC like NXP’s i.MX 8M Plus in products like the Akida Edge AI Box. It excels at tasks like vision, voice, and sensor processing, with a focus on low-power edge applications. BrainChip claims Akida can handle convolutional neural networks (CNNs) converted to SNNs, achieving high efficiency (e.g., 1–2 watts for inference), but its parameter capacity is less clear—likely in the millions to low billions when paired with a host SoC.
Comparison

1. Architecture and Compute Power

• Snapdragon: A general-purpose SoC with a powerful NPU, offering 45 TOPS (8 Gen 3) or sub-1 TOPS (Wear series). It’s optimized for dense matrix operations in deep neural networks (DNNs) and supports a wide range of AI models, including large language models (LLMs) and generative AI.
• Akida: A neuromorphic chip focused on SNNs, with lower raw compute (exact TOPS unspecified but likely <10 TOPS even with a host SoC). It’s specialized for sparse, event-driven tasks rather than general-purpose AI.
• Winner: Snapdragon for raw compute power and versatility, especially for GenAI tasks requiring billions of parameters.

2. Power Efficiency

• Snapdragon: Highly efficient for its class, with the 8 Gen 3 achieving ~0.5 watts per inference for a 1-billion-parameter model (hypothetical, based on optimizations). Wear chips are even lower power but lack the capacity for large models.
• Akida: Potentially more efficient for specific workloads due to its event-based design—consuming microjoules per spike event. For a 1-billion-parameter SNN, power could be <1 watt, but this assumes the model fits its architecture.
• Winner: Akida for ultra-low-power scenarios; Snapdragon for balanced efficiency at scale.

3. Model Capacity and GenAI Suitability

• Snapdragon: Proven to handle 1-billion-parameter models (e.g., Stable Diffusion) and up to 10 billion on high-end chips. Its memory bandwidth and NPU make it ideal for GenAI on-device.
• Akida: Limited by its neuromorphic design and reliance on a host SoC. While it can process CNNs and smaller SNNs, scaling to 1 billion parameters is unproven and likely constrained by memory (e.g., 500 MB for weights alone exceeds typical edge device RAM).
• Winner: Snapdragon, as it’s explicitly built for large-scale GenAI.

4. Use Case Fit (Watch Battery Context)

• Snapdragon: On a watch battery (e.g., 300 mAh, 1.11 Wh), a high-end Snapdragon could run thousands of inferences sporadically, but current Wear chips can’t handle 1 billion parameters due to compute and memory limits.
• Akida: Designed for edge devices, it could theoretically operate on a watch battery for lightweight SNN tasks (e.g., gesture recognition), but not for GenAI at 1 billion parameters without significant host support.
• Winner: Neither fully wins here—Snapdragon Wear falls short, and Akida isn’t suited for GenAI at this scale.

Who Wins?

• For GenAI with 1 Billion Parameters on a Watch Battery: Snapdragon is the winner. Its high-end chips (e.g., 8 Gen 3) have demonstrated this capability on smartphones, and future Wear variants could scale down with optimization. Akida’s neuromorphic approach, while innovative, lacks the proven capacity and ecosystem for such large models.
• 
  
• Overall Edge AI: Snapdragon dominates due to its versatility, ecosystem (Qualcomm AI Hub), and widespread adoption. Akida shines in niche, ultra-low-power applications like sensor processing but isn’t a direct competitor for GenAI workloads.

In short, Snapdragon wins for the specific scenario of handling 1 billion parameters for GenAI on-device, even under watch battery constraints, assuming future wearable-optimized versions. Akida is a compelling alternative for smaller, event-driven AI tasks but doesn’t match Snapdragon’s breadth or power for this use case.

I asked ChatGPT your question and I feel it is a way more balanced synopsis

: Please compare snapdragon with akida and who is the winner?


Snapdragon (by Qualcomm) and Akida (by BrainChip) are very different types of processors designed for different purposes. Here’s a detailed comparison:

1. Purpose and Architecture​

• Snapdragon: Snapdragon is a general-purpose system-on-chip (SoC) designed for mobile devices, laptops, and other consumer electronics. It includes CPU, GPU, AI engine, ISP (image signal processor), modem, and more.
• Akida: Akida is a neuromorphic processor designed specifically for low-power AI and edge computing applications. It is optimized for event-based processing and on-device learning.

2. Performance​

• Snapdragon: Offers high performance for a wide range of applications, including gaming, photography, and AI inference. It is used in flagship smartphones, tablets, and laptops.
• Akida: Specializes in ultra-low-power AI tasks, such as pattern recognition, anomaly detection, and sensor data processing. It is not designed for general-purpose computing or high-end mobile tasks.

3. AI Capabilities​

• Snapdragon: Uses a dedicated AI engine (Hexagon DSP, Tensor Accelerator, and GPU) to perform AI-based tasks like image processing, voice recognition, and natural language processing.
• Akida: Uses a neuromorphic approach, mimicking the human brain’s event-driven processing for real-time, low-power AI computations. It is highly efficient for edge AI applications.

4. Power Efficiency​

• Snapdragon: Optimized for mobile battery efficiency but consumes more power than Akida when running AI tasks.
• Akida: Extremely power-efficient, as it uses event-based processing to reduce unnecessary computations. It is ideal for battery-powered and always-on AI applications.

5. Use Cases​

• Snapdragon: Smartphones, tablets, laptops, AR/VR devices, automotive infotainment, etc.
• Akida: Edge AI applications such as security cameras, medical devices, industrial sensors, and IoT devices.

Who is the Winner?​

• If you need a powerful general-purpose processor with strong AI capabilities, Snapdragon is the better choice.
• If you need an ultra-low-power AI processor for edge computing and event-based learning, Akida is the winner.

It depends on the application! If you’re looking at mobile performance, Snapdragon wins. If you need efficient, real-time AI at the edge, Akida is the clear choice. Let me know your use case, and I can refine the comparison further!

 

[7für7]

Grok and I we are friends from the school let’s say.. we are kind of more buddies let’s say… he gave me this answer….

Alright, let’s compare the Snapdragon and Akida processors—each with their own special powers. We’ll see who might "win"!

Snapdragon: The All-Around Champ
Snapdragon is made by Qualcomm, a big company that puts these processors in tons of phones, tablets, and even some laptops. Think of Snapdragon as a superhero who’s super fast, strong, and good at lots of things—like playing games, taking pictures, and connecting to the internet.

Super Powers:
Really fast at running apps and games (like a speedster zooming around!).
Awesome graphics with its Adreno GPU—perfect for watching cartoons or playing Fortnite.
Can connect to 5G, so it’s like having the fastest Wi-Fi ever.
Used in lots of gadgets, so it’s a popular hero everyone knows.
Kid-Friendly Wow Factor: It’s like the superhero who can fly, fight, and save the day all at once. If you want to play games without lag or watch videos super quick, Snapdragon’s your buddy!
Akida: The Brainy New Kid
Akida is made by BrainChip, a smaller company with a cool twist—it’s a “neuromorphic” processor, which means it works like a human brain! Picture Akida as a smart, sneaky hero who doesn’t need tons of power but can still do amazing tricks, especially with AI (artificial intelligence).

Super Powers:
Thinks like a brain, so it’s great at learning stuff—like recognizing faces or sounds.
Uses way less energy, so it’s like a hero who never gets tired.
Perfect for small gadgets, like smart toys or cameras, that need to be clever without a big battery.
Spiking neural networks (fancy brain stuff) make it super efficient.
Kid-Friendly Wow Factor: It’s the hero who’s small but mighty, like a genius sidekick who solves puzzles and saves power. If you had a robot pet that could learn your voice, Akida might be its brain!
The Showdown: Who Wins?
For Fun and Games: Snapdragon wins! It’s like the superhero who can blast through video games and make your phone feel like a rocket. Kids love fast and flashy, and Snapdragon’s got that covered.
For Smart Stuff: Akida takes the crown! It’s the brainy hero who could power a talking teddy bear or a toy that learns your favorite color—super cool for futuristic fun.
For Battery Life: Akida’s the champ! It sips power like a tiny juice box, while Snapdragon gulps it down faster during big missions.
Kid’s Pick?
If a kid’s watching this battle, they’d probably yell, “Snapdragon’s the winner!” Why? Because it’s the big, loud hero that makes phones and games super fun and fast—stuff kids notice right away. Akida’s awesome, but its “brainy” powers are quieter and more for grown-up gadgets, so it might not get the loudest cheer from the playground crowd.

So, Snapdragon’s the superhero kids would draw with a big cape and lightning bolts, while Akida’s the clever one they’d figure out is cool later—like when they get a smart toy that actually listens to them! What do you think—would you root for the fast champ or the smart sidekick?

 

[7für7]

And additional I got this answer

Alright, let’s imagine I’m Qualcomm, having just acquired Edge Impulse to boost my edge AI game. The question is whether I’d let BrainChip, with its Akida neuromorphic tech, still play a role in my plans. Here’s how I’d think it through, keeping it strategic and straightforward.

First off, as Qualcomm, my goal is to dominate edge AI—think IoT, phones, industrial gadgets, you name it. Buying Edge Impulse gives me a slick software platform to help developers build and deploy AI models fast, especially on my Snapdragon and Dragonwing processors. It’s a big win for making my hardware more appealing and accessible. But BrainChip’s Akida is a different beast—it’s hardware, specifically neuromorphic IP, designed to mimic the brain, sip power, and handle AI tasks right at the sensor. So, do I still need them?

Here’s the deal: Snapdragon’s my bread and butter—fast, versatile, and already powering millions of devices. It’s a general-purpose champ with beefy AI capabilities via its Hexagon DSP and Adreno GPU. Akida, though, is a specialist—ultra-low power, event-based, and great for niche edge cases like always-on sensors or tiny battery-powered gadgets. If I’m Qualcomm, I’ve got to ask: does Akida fill a gap Snapdragon can’t, or is it just overlap?

Let’s break it down. Snapdragon’s awesome for high-performance stuff—gaming, 5G, video—but it’s not always the leanest on power for, say, a smart doorbell that’s awake 24/7. Akida shines there, using spiking neural networks to process only what matters, keeping power under a milliwatt. Edge Impulse already supports Akida, so my acquisition means I’ve inherited that compatibility. Why not leverage it? I could integrate Akida IP into my portfolio—maybe not for phones, where Snapdragon rules, but for IoT or wearables where power’s a dealbreaker.

Strategically, BrainChip’s not a direct threat—they’re not churning out chips to rival me; they’re licensing IP. I could buy that IP, partner with them, or even acquire them outright if the price is right. Their neuromorphic tech could be a secret weapon for my Dragonwing line, aimed at industrial and embedded AI. Imagine a Dragonwing chip with Akida baked in—low-power AI at the edge, paired with Edge Impulse’s tools. That’s a combo that could crush competitors like Nvidia in niche markets.

But there’s a flip side. I’m Qualcomm—I don’t like relying on outsiders long-term. If Akida’s tech is that good, I might rather reverse-engineer the concept or build my own neuromorphic solution in-house. I’ve got the R&D muscle, and Edge Impulse gives me the software to optimize it. Why split profits with BrainChip if I can own the whole stack? Plus, integrating Akida might confuse my branding—Snapdragon’s the star, and I don’t want some quirky brain-chip stealing its thunder.

So, here’s my play as Qualcomm: I’d keep BrainChip in the mix for now. License their Akida IP for specific low-power use cases—think smart sensors, wearables, or edge boxes—while I study it. Edge Impulse already bridges us, so it’s low risk. Meanwhile, I’d task my engineers with figuring out if I can replicate or beat Akida’s efficiency with my own twist. If BrainChip’s open to a cheap buyout, I’d snap them up to lock in the tech and talent. But if they’re stubborn or redundant, I’d phase them out once I’ve got my own neuromorphic ace up my sleeve.

Bottom line? BrainChip’s got a role as long as they bring something Snapdragon can’t—ultra-low power edge AI. But as Qualcomm, I’m not here to play nice forever—I’m here to win. So, they’d better fit my plans or get ready to be sidelined. What do you think—should I keep them close or cut them loose?

 

[manny100]

Grok and I we are friends from the school let’s say.. we are kind of more buddies let’s say… he gave me this answer….

Alright, let’s compare the Snapdragon and Akida processors—each with their own special powers. We’ll see who might "win"!

Snapdragon: The All-Around Champ
Snapdragon is made by Qualcomm, a big company that puts these processors in tons of phones, tablets, and even some laptops. Think of Snapdragon as a superhero who’s super fast, strong, and good at lots of things—like playing games, taking pictures, and connecting to the internet.

Super Powers:
Really fast at running apps and games (like a speedster zooming around!).
Awesome graphics with its Adreno GPU—perfect for watching cartoons or playing Fortnite.
Can connect to 5G, so it’s like having the fastest Wi-Fi ever.
Used in lots of gadgets, so it’s a popular hero everyone knows.
Kid-Friendly Wow Factor: It’s like the superhero who can fly, fight, and save the day all at once. If you want to play games without lag or watch videos super quick, Snapdragon’s your buddy!
Akida: The Brainy New Kid
Akida is made by BrainChip, a smaller company with a cool twist—it’s a “neuromorphic” processor, which means it works like a human brain! Picture Akida as a smart, sneaky hero who doesn’t need tons of power but can still do amazing tricks, especially with AI (artificial intelligence).

Super Powers:
Thinks like a brain, so it’s great at learning stuff—like recognizing faces or sounds.
Uses way less energy, so it’s like a hero who never gets tired.
Perfect for small gadgets, like smart toys or cameras, that need to be clever without a big battery.
Spiking neural networks (fancy brain stuff) make it super efficient.
Kid-Friendly Wow Factor: It’s the hero who’s small but mighty, like a genius sidekick who solves puzzles and saves power. If you had a robot pet that could learn your voice, Akida might be its brain!
The Showdown: Who Wins?
For Fun and Games: Snapdragon wins! It’s like the superhero who can blast through video games and make your phone feel like a rocket. Kids love fast and flashy, and Snapdragon’s got that covered.
For Smart Stuff: Akida takes the crown! It’s the brainy hero who could power a talking teddy bear or a toy that learns your favorite color—super cool for futuristic fun.
For Battery Life: Akida’s the champ! It sips power like a tiny juice box, while Snapdragon gulps it down faster during big missions.
Kid’s Pick?
If a kid’s watching this battle, they’d probably yell, “Snapdragon’s the winner!” Why? Because it’s the big, loud hero that makes phones and games super fun and fast—stuff kids notice right away. Akida’s awesome, but its “brainy” powers are quieter and more for grown-up gadgets, so it might not get the loudest cheer from the playground crowd.

So, Snapdragon’s the superhero kids would draw with a big cape and lightning bolts, while Akida’s the clever one they’d figure out is cool later—like when they get a smart toy that actually listens to them! What do you think—would you root for the fast champ or the smart sidekick?

Does it make any difference if for wearables we ask a comparison specifically to Pico?
Perhaps even more specialised eg, Pico for health wearables Vs Snapdragon 8?
Pico is based on Gen2 so it includes TENNS.

 

[manny100]

Even Pico compared to wearables for defense, I etc

 

[7für7]

Even Pico compared to wearables for defense, I etc

I think it doesn’t matter what we ask since every AI is collecting informations from different sources and put everything together line we do.. it’s just one more opinion of a so called artificial intelligence. Which doesn’t make him more trustworthy than bravo or me for example… with my expertise and deep knowledge

 

[DingoBorat]

Even Pico compared to wearables for defense, I etc

Pico isn't "smart" enough, for anything but very simple tasks, it is absolutely no comparison, to something like Snapdragon 8..

 

[manny100]

Pico wearables are more likely to be smaller and much more power efficient than Snapdragon who are a full SOC.