BRN Discussion Ongoing

[7für7]

Thanks mate. Still can't read it but I do need new glasses. Missus tried too but could only get half of it. Will keep an eye on Booz Allen. Interesting stuff.


SC

ChatGPT was able to read it



TOP TITLE:

C2BMC

Command and Control, Battle Management, and Communications

Subtitle (below C2BMC):

The Command and Control, Battle Management, and Communications (C2BMC) program is the hub of the layered Missile Defense System. It is a vital operational system that enables the President, Secretary of Defense and Combatant Commanders at strategic, regional and operational levels to systematically plan missile defense operations, to collectively see the battle develop, and to dynamically manage shielded networked sensors and weapons systems to achieve global and regional mission objectives.


TOP COMMAND STRIP (color-coded):

• NMCC
• USSPACECOM
• USNORTHCOM
• USINDOPACOM
• USEUCOM
• USCENTCOM

MAIN TITLE (center of the image):

THE SYSTEM OF ELEMENTS


DEFENSE SEGMENTS (left to right):

BOOST

Defense Segment
(Shows missiles launching)


ASCENT / MIDCOURSE

Defense Segment
(Depicts missile intercept systems)

• GBI
  Ground-Based Interceptor
• SM-3 IIA
  Standard Missile
• SM-3 IA/IB
  Standard Missile
• AEGIS
  SHIP & ASHORE
  Ballistic Missile Defense

TERMINAL

Defense Segmen

• THAAD
  Terminal High Altitude Area Defense
• SM-6
  Standard Missile
• AEGIS
  (Standard Missile)
• PAC-3
  Patriot Advanced Capability

BOTTOM SECTION: SENSORS


Title:
SENSORS

Descriptive Text:

An effective layered defense relies on timely sensor information provided by space and ground-based assets. Sensors provide critical tracking and discrimination data to help warfighters see the threat, track it and engage it effectively.

Sensor Types (with icons):

• SATELLITE SURVEILLANCE SYSTEMS
• UPGRADED EARLY WARNING RADARS
• FORWARD-BASED RADARS
• SPY RADARS
• DISCRIMINATING RADARS

 

[Fullmoonfever]

Thesis just out of KTH.

Haven't downloaded to read it but just the abstract was enough for me on the confirmation of how just our AKD1000 First Gen stacked up and clearly showed its "edge" in the edge space over NVIDIA GPU.

Think it's a given that as complexity increases the processing power shifts however the power usage is still an advantage.

Be interesting to see how it goes when they focus on fully customised models.

Would also be great to see what step up AKD1500 / Gen 2 / TENNs etc is capable of in a comparison given this was just Gen 1.

Comparison of Akida Neuromorphic Processor and NVIDIA Graphics Processor Unit for Spiking Neural Networks

kth.diva-portal.org

Comparison of Akida Neuromorphic Processor and NVIDIA Graphics Processor Unit for Spiking Neural Networks

Chemnitz, Carl​

KTH, School of Electrical Engineering and Computer Science (EECS).

Ermis, Malik​

KTH, School of Electrical Engineering and Computer Science (EECS).

2025 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Jämförelse av neuromorfisk processor Akida och NVIDIA grafikkort för Spiking Neural Networks (Swedish)

Abstract [en]​

This thesis investigates the latency, throughput and energy efficiency of the BrainChip Akida AKD1000 neuromorphic processor compared to a NVIDIA GeForce GTX 1080 when running two different spiking neural network models on both hardwares. Spiking neural networks is a subset of neural networks that are specialized for neuromorphic processor. The first model is a simple image classification model (GXNOR on MNIST), and the second is a more complex object detection model (YOLOv2 on Pascal VOC). The models were trained and quantized to 2-bit and 4-bit weight precision, respectively, enabling spiking execution both on Akida AKD1000 and on GTX 1080, for the GPU CUDA was used. Results show that Akida achieved significant reductions in energy consumption and clock cycles for both models, consistent with prior findings within the field. Specifically, for the simple classification model the AKD1000 achieved 99.5 % energy reduction with 76.7 % faster inference times, despite having a clock rate 91.5 % slower than the GPU. However, for the more complex object detection model, the Akida took 118.1 % longer per inference, while reducing the energy expenditure by 96.0 %. For the MNIST model the AKD1000 showed no correlation in both cycles & time and cycle & energy. While for the YOLOv2 model it had a 0.2 correlation for both previous mentioned ratios. Suggesting that as model complexity increases, the Akida’s behaviour converges toward the GPU’s linear correlation patterns. In conclusion, the AKD1000 processor demonstrates clear advantages for low-power, edge-oriented applications where latency and efficiency are critical. However, these benefits diminish with increasing model complexity, where GPUs maintain superior scalability and performance. Due to limited documentation of the chosen models, a 1-to-1 comparison was not possible. Future work should focus on fully customized models to further explore the dynamics.

 

[manny100]

Thesis just out of KTH.

Haven't downloaded to read it but just the abstract was enough for me on the confirmation of how just our AKD1000 First Gen stacked up and clearly showed its "edge" in the edge space over NVIDIA GPU.

Think it's a given that as complexity increases the processing power shifts however the power usage is still an advantage.

Be interesting to see how it goes when they focus on fully customised models.

Would also be great to see what step up AKD1500 / Gen 2 / TENNs etc is capable of in a comparison given this was just Gen 1.

Comparison of Akida Neuromorphic Processor and NVIDIA Graphics Processor Unit for Spiking Neural Networks

kth.diva-portal.org

Comparison of Akida Neuromorphic Processor and NVIDIA Graphics Processor Unit for Spiking Neural Networks

Chemnitz, Carl​

KTH, School of Electrical Engineering and Computer Science (EECS).

Ermis, Malik​

KTH, School of Electrical Engineering and Computer Science (EECS).

2025 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Jämförelse av neuromorfisk processor Akida och NVIDIA grafikkort för Spiking Neural Networks (Swedish)

Abstract [en]​

This thesis investigates the latency, throughput and energy efficiency of the BrainChip Akida AKD1000 neuromorphic processor compared to a NVIDIA GeForce GTX 1080 when running two different spiking neural network models on both hardwares. Spiking neural networks is a subset of neural networks that are specialized for neuromorphic processor. The first model is a simple image classification model (GXNOR on MNIST), and the second is a more complex object detection model (YOLOv2 on Pascal VOC). The models were trained and quantized to 2-bit and 4-bit weight precision, respectively, enabling spiking execution both on Akida AKD1000 and on GTX 1080, for the GPU CUDA was used. Results show that Akida achieved significant reductions in energy consumption and clock cycles for both models, consistent with prior findings within the field. Specifically, for the simple classification model the AKD1000 achieved 99.5 % energy reduction with 76.7 % faster inference times, despite having a clock rate 91.5 % slower than the GPU. However, for the more complex object detection model, the Akida took 118.1 % longer per inference, while reducing the energy expenditure by 96.0 %. For the MNIST model the AKD1000 showed no correlation in both cycles & time and cycle & energy. While for the YOLOv2 model it had a 0.2 correlation for both previous mentioned ratios. Suggesting that as model complexity increases, the Akida’s behaviour converges toward the GPU’s linear correlation patterns. In conclusion, the AKD1000 processor demonstrates clear advantages for low-power, edge-oriented applications where latency and efficiency are critical. However, these benefits diminish with increasing model complexity, where GPUs maintain superior scalability and performance. Due to limited documentation of the chosen models, a 1-to-1 comparison was not possible. Future work should focus on fully customized models to further explore the dynamics.

I think AKIDA 1500 is a big step up from the 1000.
One thing that we learnt from taping out the AKIDA 1000 chip was that minor improvements can be made with little cost to the original taping but major adjustments like the AKIDA1500 has to be completely 'retaped' from scratch which is expensive.
The fast pace of change weighed against taping out costs was likely at least a small factor in not taping out Gen2.
AKIDA 1000 and 1500 gets the chips 'out there'.
I am surprised we have not heard more about engagements utilising the1500.
Bascom Hunter purchased $100k worth.
8 bit processing, improved efficiency and the VIT of the1500 is a big step up IMO.

 

[IloveLamp]

Loading…

investors.indie.inc

We are thrilled to announce that emotion3D has entered into a definite agreement to be acquired by indie.inc (Nasdaq: INDI) a global leader in automotive innovation. | emotion3D

We are thrilled to announce that emotion3D has entered into a definite agreement to be acquired by indie.inc (Nasdaq: INDI) a global leader in automotive innovation. The acquisition is subject to regulatory clearance and expected to close in the 4th quarter of this year. This marks a...

www.linkedin.com

 

[manny100]

Shorts are up significantly from a low of 1.42% on May 1st 2025 to 3.05% on 1st August with a steep slope up.
Explains the huge increase in downrapmer activity on the crapper and the re-appearance of a down ramper or 2 here recently.
Eventual short covering will see a coiled spring release push up.
Traders adding to the downramping to get in at a lower price to make the most of the eventual spike.
Likely expected pressure bought by LDA sales has shorters and traders interested. So far the SP has held around 20 ish.
We just have to ride this out. Its no issue concerning our progress or tech.
A little more good news this summer will help, eg progress on Gen 3 , Gen AI, quality partners added.

 

[IloveLamp]

Loading…

investors.indie.inc

We are thrilled to announce that emotion3D has entered into a definite agreement to be acquired by indie.inc (Nasdaq: INDI) a global leader in automotive innovation. | emotion3D

We are thrilled to announce that emotion3D has entered into a definite agreement to be acquired by indie.inc (Nasdaq: INDI) a global leader in automotive innovation. The acquisition is subject to regulatory clearance and expected to close in the 4th quarter of this year. This marks a...

www.linkedin.com

View attachment 89403

Interesting the amount of BRN partners being absorbed by the big end of town.........off the top of my head -

Qualcomm $ edge impulse
Indie $ Emotion3d
AMD $ XILNIX (admittedly an older one)
Nvidia $ ARM (Unsuccessfully)

And I'm sure there are more i haven't mentioned

 

[Mccabe84]

Interesting the amount of BRN partners being absorbed by the big end of town.........off the top of my head -

Qualcomm $ edge impulse
Indie $ Emotion3d
AMD $ XILNIX (admittedly an older one)
Nvidia $ ARM (Unsuccessfully)

And I'm sure there are more i haven't mentioned

I agree. Do you think BRN will be acquired at some point ?

 

[IloveLamp]

I agree. Do you think BRN will be acquired at some point ?

2 years ago i would've said definitely, but now I'm not so sure.....

If it was going to happen one would assume it would've already happened.

But you cannot buy what is not for sale, and that to me is the most logical explanation as to why we haven't been bought (considering how many irons we have in the fire)

All conjecture of course.

 

[FuzM]

Interesting the amount of BRN partners being absorbed by the big end of town.........off the top of my head -

Qualcomm $ edge impulse
Indie $ Emotion3d
AMD $ XILNIX (admittedly an older one)
Nvidia $ ARM (Unsuccessfully)

And I'm sure there are more i haven't mentioned

Adding one to the list

Blue Ridge Envisioneering $ Parsons Corporation

 

[7für7]

WHOOP WHOOOP!!

 

[HopalongPetrovski]

Ah,....Hop. Nice try.

Growing interest. It will be growing interest in Brainchip that increases volume.

Actually I have no idea what the catalyst will be. I just know the volume will be huge.

Hi Dipp.
The question asked was in relation to....... on that day the market was up, but BrainChip was down.....
ergo....what does it take (to improve the share price)?

Huge volume (in trades) will of course occur with any rapid or sustained increasing share price but that volume will likely be in reaction to the change rather than causal.

Growing interest will come with commercial viability as evidenced by people paying us money for our product.

To say that volume will be what causes our share price to increase is like saying planes fly because they are at 30,000 feet.
It's the thrust and lift that gets them there.

 

[Diogenese]

I think AKIDA 1500 is a big step up from the 1000.
One thing that we learnt from taping out the AKIDA 1000 chip was that minor improvements can be made with little cost to the original taping but major adjustments like the AKIDA1500 has to be completely 'retaped' from scratch which is expensive.
The fast pace of change weighed against taping out costs was likely at least a small factor in not taping out Gen2.
AKIDA 1000 and 1500 gets the chips 'out there'.
I am surprised we have not heard more about engagements utilising the1500.
Bascom Hunter purchased $100k worth.
8 bit processing, improved efficiency and the VIT of the1500 is a big step up IMO.

Hi manny,

8-bit and VIT are in Akida 2.

1500 is just Akida 1 without the ARM Cortex processor. The NPUs are basically the same as Akida 1. It relies on an external processor for configuration. It is made by Global Foundries in 22nm FD-SoI which makes it a bit faster compared to 28nm and more power efficient (less leakage losses) than vanilla CMOS.

8-bit makes it easier to run more 3rd party models on Akida 2.

Akida 2 with the TENNs model also has long skip which cuts out reprocessing already classified blocks of data by bypassing subsequent layers and sending these blocks to the output. The TENNs model is loaded in one of the 4 NPUs (aka: NPEs) in each node. The TENNs model can be run on a non-Akida processor.

 

[jrp173]

I agree. Do you think BRN will be acquired at some point ?

We can only hope so!

 

[Space Cadet]

That's me in the back, in that little black dress.

In charge of Human Resources are we Hop?

SC

 

[7für7]

We can only hope so!

We can hope for many things at this stage here… so let’s hope our hopes will come true! Hopefully

 

[HopalongPetrovski]

In charge of Human Resources are we Hop?

SC

I'm the new morale officer.

 

[manny100]

Akida Cloud isn’t just a tech upgrade—it’s a sales enabler. If BrainChip plays its cards right, this could be a turning point in how its neuromorphic AI reaches the market.

 

[manny100]

Revolutionize AI with Akida in the Cloud
" The Akida Cloud is the game-changer in edge innovation, which provides scalability, remote access, and agility required by current AI development practices."
" This is abreakthrough moment in Artificial Intelligence and edge computing as developers all over the world could now work with BrainChip directly through instant access to and prototyping with the second-generation Akida 2 system, without ever having to wait on physical hardware or having to address all those neural chip challenges with integration finally."

 

[smoothsailing18]

ANDES RISC-V CON Beijing​

Join us at the 2025 Andes RISC-V CON Beijing event on August 27 where BrainChip will demonstrate our demonstrate BrainChip’s Akida™ AKD1500 on Andes’ QiLai Voyager Board and AndesCore™ AX45MP 64-bit multicore CPU IP. We will also participate in a RISC-V ecosystem panel discussion on Accelerating AI Computing: Strengths and Challenges of RISC-V.”

RISC-V has become increasingly popular in China since the US China technology restrictions.

My opinion only DYOR

Fact Finder

 

[manny100]

Hi manny,

8-bit and VIT are in Akida 2.

1500 is just Akida 1 without the ARM Cortex processor. The NPUs are basically the same as Akida 1. It relies on an external processor for configuration. It is made by Global Foundries in 22nm FD-SoI which makes it a bit faster compared to 28nm and more power efficient (less leakage losses) than vanilla CMOS.

8-bit makes it easier to run more 3rd party models on Akida 2.

Akida 2 with the TENNs model also has long skip which cuts out reprocessing already classified blocks of data by bypassing subsequent layers and sending these blocks to the output. The TENNs model is loaded in one of the 4 NPUs (aka: NPEs) in each node. The TENNs model can be run on a non-Akida processor.

Ooops, thanks for the correction. Than that makes the AKIDA cloud look even better.