Media Alert: BrainChip Demonstrates Neuromorphic AI Capabilities of its Akida Platform at tinyML Summit 2022

[Fullmoonfever]

News & Media for Brainchip Holdings Ltd (BRCHF)​

Wed Mar 23rd, 2022 8:00am

Media Alert: BrainChip Demonstrates Neuromorphic AI Capabilities of its Akida Platform at tinyML Summit 2022​

LAGUNA HILLS, CA / ACCESSWIRE / March 23, 2022 / BrainChip Holdings Ltd (ASX:BRN) (OTCQX:BRCHF) (ADR:BCHPY), the world's first commercial producer of ultra-low power neuromorphic AI chips and IP, is exhibiting the latest capabilities of its Akida™ neuromorphic technology in person at tinyML Summit 2022. TinyML Summit will feature technical progress and ecosystem development within the "tiny" machine learning industry to enable ultra-low power ML in edge applications.
tinyML Summit 2022
Date: March 28-30, 2022
Location: Hyatt Regency San Francisco Airport
1333 Bayshore Highway
Burlingame, CA 94010
Tiny ML is a growing field of ML technologies including hardware (dedicated integrated circuits), algorithms, and software capable of performing on-device data analytics at extremely low power to enable a range of always-on applications, primarily in battery-operated devices. Tiny ML is common in audio, visual, navigational, biometric, and medical devices as well as other commercial and industrial uses.
"BrainChip appreciates the opportunity to showcase our achievements in neuroprocessing at the edge that enable even lower-power, higher-performing AI in audio, vision, olfactory, lidar and other edge sensors," said Kristofor Carlson, BrainChip's Manager of Applied Research.
Kristofor Carlson will lead the company's Poster sessions throughout the conference. Additionally, company representatives will be demonstrating the latest advancements of BrainChip's neuromorphic computing solutions in the Exhibit space.
Those interested in speaking with BrainChip at tinyML Summit 2022 can contact sales@brainchipinc.com to schedule an appointment.
About tinyML Summit 2022
tinyML Summit 2022 allows attendees to take part in the sharing, learning, and celebrating tinyML. With ever more pervasive advances in technology and algorithms, tinyML is rapidly becoming a reality. The incredibly open and collaborative nature of ML technology allows this field to advance so quickly. From its inception in 2019, the tinyML community has grown tremendously and has benefited greatly by supporting one another. Through leveraging the collective knowledge of the community, the Summit presents attendees with the opportunity to learn new ideas and approaches to solve problems and become more effective and efficient.
About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)
BrainChip is a global technology company that is producing a groundbreaking neuromorphic processor that brings artificial intelligence to the edge in a way that is beyond the capabilities of other products. The chip is high performance, small, ultra-low power and enables a wide array of edge capabilities that include on-chip training, learning and inference. The event-based neural network processor is inspired by the spiking nature of the human brain and is implemented in an industry standard digital process. By mimicking brain processing BrainChip has pioneered a processing architecture, called Akida™, which is both scalable and flexible to address the requirements in edge devices. At the edge, sensor inputs are analyzed at the point of acquisition rather than through transmission via the cloud to a data center. Akida is designed to provide a complete ultra-low power and fast AI Edge Network for vision, audio, olfactory and smart transducer applications. The reduction in system latency provides faster response and a more power efficient system that can reduce the large carbon footprint of data centers.
Additional information is available at https://www.brainchipinc.com
Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc
Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006
Contacts
Media Contact:
Mark Smith
JPR Communications
818-398-1424
Investor Contact:
Mark Komonoski
Integrous Communications
Direct: 877-255-8483
Mobile: 403-470-8384
mkomonoski@integcom.us
SOURCE: BrainChip Holdings Ltd.

 

[Dougie54]

Bring it on

 

[Fullmoonfever]

KC's poster presso for the TinyML session if not already posted.

Summit 2022 | tinyML Foundation

TinyML Summit. The topic is advances in ultra-low power Machine Learning technologies and applications.

www.tinyml.org

I highlighted one note that they are still listing the USB as well as PCIe bottom left.

 

[Fact Finder]

THIS IS HUGE NEWS - WHY WAS I NOT TOLD - BECAUSE I AM JUST A RETAIL SHAREHOLDER - THIS IS ANOTHER PILLAR SUPPORTING
MY FUTURE GENERATIONAL WEALTH TRAJECTORY.

"AKIDA NET
Recently, we have developed a replacement for the popular MobileNet v1 model used as a backbone in many applications that we call AkidaNet. AkidaNet’s architecture utilizes the Akida hardware more efficiently. Some of our preliminary results are shown below for object classification, face recognition, and face detection. In many cases, switching from MobileNet v1 to AkidaNet results in a slight increase in speed and accuracy accompanied by a 15% to 30% decrease in power usage."

How mind blowing important is a 30% decrease in already ludicrously low power usage at the Edge.

I think we need to start a list of those who want to purchase an EQXX.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[Slade]

KC's poster presso for the TinyML session if not already posted.

Summit 2022 | tinyML Foundation

TinyML Summit. The topic is advances in ultra-low power Machine Learning technologies and applications.

www.tinyml.org

I highlighted one note that they are still listing the USB as well as PCIe bottom left.

View attachment 3130

Fullmoon, Thank you for pointing out the USB, I would never have seen that. Perhaps it is still in the mix and will be released soon.

 

[Slade]

Does anyone know why the Akida USB has not come to market. I think it was meant to be available by now for a price of around $40-80.
I ask because I want one.

 

[Fullmoonfever]

Fullmoon, Thank you for pointing out the USB, I would never have seen that. Perhaps it is still in the mix and will be released soon.

Yeah, was something that caught my eye as waiting for something more formal to be released by the Co.

Was good to see it included.

 

[uiux]

THIS IS HUGE NEWS - WHY WAS I NOT TOLD - BECAUSE I AM JUST A RETAIL SHAREHOLDER - THIS IS ANOTHER PILLAR SUPPORTING
MY FUTURE GENERATIONAL WEALTH TRAJECTORY.

"AKIDA NET
Recently, we have developed a replacement for the popular MobileNet v1 model used as a backbone in many applications that we call AkidaNet. AkidaNet’s architecture utilizes the Akida hardware more efficiently. Some of our preliminary results are shown below for object classification, face recognition, and face detection. In many cases, switching from MobileNet v1 to AkidaNet results in a slight increase in speed and accuracy accompanied by a 15% to 30% decrease in power usage."

How mind blowing important is a 30% decrease in already ludicrously low power usage at the Edge.

I think we need to start a list of those who want to purchase an EQXX.

My opinion only DYOR
FF

AKIDA BALLISTA

Fyi, AkidaNet has been out for a while in MetaTF with the other models you have identified, eg. Melanoma identification etc

Recently I have updated two of my repositories to support Akida hardware (now that I have a chip). One is using AkidaNet.

GitHub - stdp/akida-camera: Learning how to use the Akida neuromorphic processor with a Camera feed

Learning how to use the Akida neuromorphic processor with a Camera feed - stdp/akida-camera

github.com

GitHub - stdp/akida-yolo: A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame.

A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame. - stdp/akida-yolo

github.com

This video was made using the above project, using the face detection branch:

GitHub - stdp/akida-yolo at feature/yolo-face

A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame. - GitHub - stdp/akida-yolo at feature/yolo-face

github.com

 

[Andy38]

Fyi, AkidaNet has been out for a while in MetaTF with the other models you have identified, eg. Melanoma identification etc

Recently I have updated two of my repositories to support Akida hardware (now that I have a chip). One is using AkidaNet.

GitHub - stdp/akida-camera: Learning how to use the Akida neuromorphic processor with a Camera feed

Learning how to use the Akida neuromorphic processor with a Camera feed - stdp/akida-camera

github.com

GitHub - stdp/akida-yolo: A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame.

A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame. - stdp/akida-yolo

github.com

This video was made using the above project, using the face detection branch:

GitHub - stdp/akida-yolo at feature/yolo-face

A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame. - GitHub - stdp/akida-yolo at feature/yolo-face

github.com

Thanks for sharing @uiux, absolutely bloody love this technology and it’s apparent ease of use and application to almost anything!
Stick tight holders, this has been a fun ride thus far, with Rob T’s words of “Tip if the iceberg” and “I’m excited” resonating in my head while writing this haha.
Enjoy the weekend, I’m off to see my family in NZ for the first time in 2 1/2 years- my parents will finally get to meet their newest but not so new grandchild!

Andy

 

[Fact Finder]

Fyi, AkidaNet has been out for a while in MetaTF with the other models you have identified, eg. Melanoma identification etc

Recently I have updated two of my repositories to support Akida hardware (now that I have a chip). One is using AkidaNet.

GitHub - stdp/akida-camera: Learning how to use the Akida neuromorphic processor with a Camera feed

Learning how to use the Akida neuromorphic processor with a Camera feed - stdp/akida-camera

github.com

GitHub - stdp/akida-yolo: A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame.

A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame. - stdp/akida-yolo

github.com

This video was made using the above project, using the face detection branch:

GitHub - stdp/akida-yolo at feature/yolo-face

A basic example of using Akida with a camera feed running YOLO to identify people and cars within frame. - GitHub - stdp/akida-yolo at feature/yolo-face

github.com

I assumed as much - Hence my “Why was I not told” - publishing these details hidden in MetaTF is not reaching 99% of shareholders???

Thanks for the great video.

My opinion only DYOR
FF

AKIDA BALLISTA

 

[JoMo68]

Thanks for sharing @uiux, absolutely bloody love this technology and it’s apparent ease of use and application to almost anything!
Stick tight holders, this has been a fun ride thus far, with Rob T’s words of “Tip if the iceberg” and “I’m excited” resonating in my head while writing this haha.
Enjoy the weekend, I’m off to see my family in NZ for the first time in 2 1/2 years- my parents will finally get to meet their newest but not so new grandchild!

Andy

We’re going on the 8th - can’t wait!!

 

[Murphy]

Thanks for sharing @uiux, absolutely bloody love this technology and it’s apparent ease of use and application to almost anything!
Stick tight holders, this has been a fun ride thus far, with Rob T’s words of “Tip if the iceberg” and “I’m excited” resonating in my head while writing this haha.
Enjoy the weekend, I’m off to see my family in NZ for the first time in 2 1/2 years- my parents will finally get to meet their newest but not so new grandchild!

Andy

Good luck with the trip Andy. I'll bet your folks are uber excited.