DingoBorat
Slim
Okay, so I've covered myself in that stuff and she still won't talk to me..
It's supposed to be some kind of "Mermaid attractant" right??..
Or.. Oh no.. I've got it wrong haven't I...
BRN Discussion Ongoing
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It's supposed to be some kind of "Mermaid attractant" right??..
Or.. Oh no.. I've got it wrong haven't I...
Fullmoonfever
Top 20
Think we must be losing our dot joining skills....this release beginning of Dec shows DeGirum already supporting our AkidaWas trying to understand DeGirum a bit more and found this article by Max Maxfield who we know has written some succinct and positive articles on Akida too.
This article, whilst a couple years old, gives a pretty good insight into what they can do and I believe they have further expanded their offerings. Good partnership imo.
DeGirum Has the Best Edge AI Story Ever!
I sometimes feel like I’m listening to a broken record when a company calls to tell me about the wonders of their new artificial intelligence (AI) processor. I try to say “Ooh” and “Aah” in all the…www.eejournal.com
January 12, 2023
DeGirum Has the Best Edge AI Story Ever!
by Max Maxfield
I sometimes feel like I’m listening to a broken record when a company calls to tell me about the wonders of their new artificial intelligence (AI) processor. I try to say “Ooh” and “Aah” in all the right places, but oftentimes my heart isn’t really in it. Every now and then, however, I do get to hear something that makes me sit up in my seat and exclaim, “WHAT? O-M-G!”
I honestly believe that “WHAT? O-M-G!” is what you are going to say upon reading this column, but before we plunge into the fray with gusto and abandon…
Rarely does a day go by that I’m not exposed to some hot-off-the-press AI-related topic. For example, my chum Joe Farr just emailed me to say that he asked the ChatGPT chatbot, “Could you write a program in C# that acts as a BASIC interpreter?” As Joe informed me, he was surprised by the response, which I’ve summarized as follows:
I’ve documented Joe’s experiments in more detail in a Cool Beans Blog: Using ChatGPT to Write a BASIC Interpreter. As part of that blog, I also made mention of the new American science fiction horror film M3GAN (pronounced “Megan”), the watching of which will also have you sitting up in your seat exclaiming “WHAT? O-M-G!” (I speak from experience).
Returning to the topic in hand, I was just chatting with the cofounders of a company that has only recently emerged from stealth mode. Winston Lee (CEO) and Shashi Chilappagari (CTO) founded DeGirum.ai in 2017, and the company is already offering sample modules featuring their ORCA-NNX (Neural Network Express) device.
“Good Grief, not another AI accelerator,” I hear you muttering under your breath. Trust me, what I’m about to tell you will dispel your cynical attitude and leave you clamoring for more, in which case, you’re welcome. Of course, if you build your own AI accelerators, you may be less enthused by the news I am about to impart, in which case, I’m sorry.
As Winnie-the-Pooh famously said, “For I am a bear of very little brain, and long words bother me.” I also am a bear of little brain, and I feel the same way about long presentations. I like a simple story that I can wrap my brain around, so it can be disconcerting when a company assaults me with a presentation featuring so many slides that I start to fear it will never end. Thus, you can only imagine my surprise and delight when I was presented with DeGirum’s 4-slide offering.
We commenced with Slide #1 as shown below. Shashi made mention of the fact that trying to work out whose AI accelerator was the best is a non-trivial task, not the least that the concept of “best” may vary from model-to-model and application-to-application.
Evaluating AI hardware is hard (Source: DeGirum.ai)
It doesn’t help when some people talk in terms of tera operations per second (TOPS) while others make their claims based on frames per second (FPS). And then there are those who boast of their offering with tall tales of TOPS/Watt, TOPS/$, FPS/Watt, and FPS/Dollar. Just to add to the fun and frivolity, we may not end up talking in terms of real units because cunning marketeers are not averse to “normalizing” (some may say “obfuscating”) their metrics in terms of price, power, or performance.
As Pontius Pilate said (well, warbled) in Jesus Christ Superstar: “And what is ‘truth’? Is truth unchanging law? We both have truths. Are mine the same as yours?” If I didn’t know better, I’d say Pontius had spent too much time perusing data sheets from different AI accelerator vendors.
Even the simplest things can be hard to measure, and the aforementioned terms don’t mean the same for everyone. One AI accelerator’s data sheet may quote twice the TOPS of someone else’s offering, but that doesn’t mean the end application will run twice as fast.
Take TOPS/Watt, for example. Are we just talking about the matrix multiplication unit, or the whole system-on-chip (SoC) power, and are we also taking any DDR memory, PCIe, and other peripherals into account? These nitty-gritty details are rarely made clear in a product sheet.
If we are looking at statistics associated with an accelerator, we may not obtain the same results with different host processors. Even values associated with the accelerator itself can vary from model to model. And, perhaps most importantly, the real application is never running only the AI model. In the case of a visual processing task, for example, the application will also be doing things like decoding, resizing, and otherwise processing a video feed and making decisions and performing actions based on what the model detects.
ResNet-50 is a 50-layer convolutional neural network (48 convolutional layers, one MaxPool layer, and one average pool layer). If someone says “We can run XXX frames per second of a ResNet-50 model” in their AI accelerator datasheet, this doesn’t actually mean much of anything to an end application developer.
And, if you are architecting a new system, there are a bunch of potential hardware restrictions that have to be taken into account. What types and sizes of model can the AI accelerator accommodate? How about input size, precision, and the number of models that can be running at the same time?
As a parting thought before we move on, how would one set about comparing an AI accelerator that is very efficient but limited in what it can run to an accelerator that can run almost anything but is not as efficient. Which is best? How long is a piece of string?
Well, we all know how difficult this stuff can be, but it’s refreshing to hear a vendor saying it out loud. By this time, I was nodding my head and agreeing with everything Shashi and Winston said, at which point they whipped out Slide 2 showing their ORCA-NNX chips on two M.2 modules (one with external DDR and one without).
ORCA-NNX chips presented on M.2 modules with and without DRAM
(Source: DeGirum.ai)
This is when they started to batter me with information pertaining to the ORCA-NNX, which—I now know—supports vision models and speech models (and other models), large models and small models, pruned models and dense models, floating-point models (Float32) and quantized models (Int8), and that’s just the beginning. In addition to DRAM support, along with PCIe and USB interfaces, ORCA-NNX also supports efficient model multiplexing, multi-camera real-time performance, and multi-chip scaling, where the latter means you may run one or two (or more) ORCA-NNX accelerators in an edge device, or even more ORCA-NNX accelerators in an edge server, all without changing the software.
And, speaking of software, we are talking about a fully pipelined AI server stack, simple and convenient Python and C++ SDKs, intuitive APIs for rapid application development, model pipelining and parallelization, support for a wide variety of input data types including (but not limited to) images, camera feeds, audio and video streams, and video files, all coupled with automatic and efficient scaling for multiple devices.
“Hmmm, this all sounds mighty impressive,” I thought, “but what does the ORCA-NNX architecture look like and how does this compare to the competition?” I think Shashi was reading my mind, because this is where we transitioned to Slide #3 as shown below. Well, not exactly as shown below, because I’ve blurred out the company and product names associated with any competitors. The folks at DeGirum say: “When we publish numbers officially, it is our duty to report the conditions in which we measured them or if we have obtained them from other sources,” and they prefer to keep some information on the down-low for the moment. They did reveal that the vertical axis is in terms of FPS, if that helps.
Application performance (Source: DeGirum.ai)
From PyTorch we are informed that, “YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset and including simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML, and TFLite.”
The results from YOLOv5s using single, dual, and quad ORCA-NNX implementations are shown in the three columns on the right. As we see, even a single ORCA-NNX outperforms its nearest competitor.
So, where we are in this column at this moment in time is we’ve been told that (a) it’s very hard to compare different AI accelerators, (b) everyone quotes different numbers, (c) DeGirum’s ORCA-NNX outperforms the competition, and (d) DeGirum marry their powerful hardware with intuitive and easy-to-use software that helps developers to quickly create powerful edge AI applications.
“But these are the same claims everyone makes,” I thought to myself, with a sad (internal) grimace.
“But these are the same claims everyone makes,” said Shashi, with a wry smile.
Not surprisingly, this is a problem the guys and gals at DeGirum encountered very early on. They would meet and greet a new potential customer, explain that they had the best hardware and even better software, and the customer would immediately respond that everyone else was saying exactly the same thing.
This is when Shashi introduced Slide #4 (he did this with something of a flourish and with a silent “Tra-la,” if the truth be told). In order to silence the skeptics, the chaps and chapesses at DeGirum have introduced something they call the DeLight Cloud Platform as shown below:
DeLight Cloud Platform (Source: DeGirum.ai)
And what a delight it is (I’m sorry, I couldn’t help myself). The idea behind the DeLight Cloud Platform is that, rather than simply say “we are the best,” the folks at DeGirum decided to let users prove this for themselves.
As Shashi told me, people want to be able to compare and contrast AI accelerator application performance, but doing this in a rigorous manner means purchasing a bunch of platforms, creating software, porting it to all the platforms, checking that everything is working as planned, and performing an analysis in terms of power, price, and performance to see which one comes out on top. Not surprisingly, however, obtaining the hardware, loading the necessary drivers, installing the full software tool chain, and developing even a simple test application can consume a substantial amount of time and resources.
All of this is addressed by the DeLight Cloud Platform. First, we have the DeGirum Device Farm which includes ORCA-NNX platforms from DeGirum, Edge TPUs from Google, and GPU lines from Nvidia, along with VPUs (a general term for accelerators coming out from Intel), ARM-based SoCs, and others that will be added as their hardware and software becomes more widely available.
Next, we have the DeGirum Model Zoo, which boasts a collection of ready-to-use models. Users can employ DeGirum’s hardware-agnostic APIs to start developing applications on their client-side Windows, Unix/Linux, and Mac hosts. In the not-so-distant future, the folks at DeGirum plan to add a BYOM (“bring your own model”) capability.
All this means users can load a model and instantly start developing a test application. Once they have their application working, they can use the DeLight Cloud Platform to evaluate all of the hardware options.
One of the major pain points in today’s model design, training, and porting processes is that, by the time you try to make your model work on your selected hardware, it may be too late to discover that the model is not, in fact, well suited to that hardware. The folks at DeGirum say they’ve flipped the process around so you can start with the hardware first.
The primary difference between a trained and untrained model are its weights, which means you can evaluate an untrained model’s compatibility, speed, and portability on day one without having to train the little rascal. Once you know your model will work on the selected hardware, you can train it, prune it, quantize it, etc. And, once you’ve developed your real-world application on the DeLight Cloud Platform, you can use the same software to deploy this application onto your edge devices.
The applications for edge AI are essentially limitless, including surveillance, smart homes, smart buildings, smart cities, robotics, industry, medical, manufacturing, agriculture (e.g., AI-equipped drones detecting weeds or fungal outbreaks and applying pesticides and insecticides in small, focused doses—see Are Agricultural Drones Poised to be the Next Big Thing?).
Creating state-of-the-art edge AI accelerators provides tremendous opportunities for small, innovative companies like DeGirum, but only if they can convince users that their technology is worth investigating. As far as I’m concerned, offering the DeLight Cloud Platform is a masterstroke (I’m reminded of the Brilliant! Guinness adverts because I want to exclaim “Brilliant!”). What say you?
I also found it interesting that DeGirum use M.2 form factor with Orca and now we've said we're doing M.2
DeGirum Delivers Industry's First Hardware-Agnostic PySDK - Simplifies Edge AI Development
/PRNewswire/ -- DeGirum®, a leader in edge AI, announced the release of its hardware-agnostic PySDK, designed to streamline AI development at the edge....
DeGirum Delivers Industry's First Hardware-Agnostic PySDK - Simplifies Edge AI Development
News provided by
DeGirum Corp.Dec 03, 2024, 07:00 ET
Focused on Vision Processing, Industrial, Automotive, and Smart Retail Applications Development
SANTA CLARA, Calif., Dec. 3, 2024 /PRNewswire/ -- DeGirum®, a leader in edge AI, announced the release of its hardware-agnostic PySDK, designed to streamline AI development at the edge. DeGirum's PySDK provides a unified software stack that allows developers to add AI to any application with just two lines of code. PySDK supports a wide range of hardware:
Accelerators
- NVIDIA® GPUs
- Hailo®
- MemryX®
- Google® Edge TPU
- BrainChip®
- Intel®
- AMD®
- Texas Instruments®
- NXP®
- Rockchip®
- Raspberry Pi®
7für7
Top 20
Christopher McMillan on LinkedIn: #ces2025 #ai #laptop #smartglasses #ai #gaming #robotics
#CES2025 #AI #Laptop #SmartGlasses #AI #Gaming #Robotics Here's a summary of the highlights from CES 2025 Day 3: Tech Innovations and Gadgets: Lenovo's…
Fullmoonfever
Top 20
To understand a bit more on what they do, here's a vid from mid 24.Think we must be losing our dot joining skills....this release beginning of Dec shows DeGirum already supporting our Akida
I also found it interesting that DeGirum use M.2 form factor with Orca and now we've said we're doing M.2
DeGirum Delivers Industry's First Hardware-Agnostic PySDK - Simplifies Edge AI Development
/PRNewswire/ -- DeGirum®, a leader in edge AI, announced the release of its hardware-agnostic PySDK, designed to streamline AI development at the edge....www.prnewswire.com
DeGirum Delivers Industry's First Hardware-Agnostic PySDK - Simplifies Edge AI Development
News provided by
DeGirum Corp.
Dec 03, 2024, 07:00 ET
Focused on Vision Processing, Industrial, Automotive, and Smart Retail Applications Development
SANTA CLARA, Calif., Dec. 3, 2024 /PRNewswire/ -- DeGirum®, a leader in edge AI, announced the release of its hardware-agnostic PySDK, designed to streamline AI development at the edge. DeGirum's PySDK provides a unified software stack that allows developers to add AI to any application with just two lines of code. PySDK supports a wide range of hardware:
Accelerators
Application Processors
- NVIDIA® GPUs
- Hailo®
- MemryX®
- Google® Edge TPU
- BrainChip®
- Intel®
- AMD®
- Texas Instruments®
- NXP®
- Rockchip®
- Raspberry Pi®
They demo how the system works from about the 31 min mark. Pretty cool stuff and with us onboard too, means Devs out there can really start to play with Akida, mark it against other accelerators etc which hopefully will excite them further as to what we can do and entice more enquiries and sales.
Maybe 4 ?
Luppo71
Founding Member
Great find mate
"Apple pro earbuds have put this technology in theirs"
Surely this is a huge takeaway from this interview even if they are just currently testing it out.
MDhere
Top 20
Thanks Slade, Wow, within 5 years " I see it embedded in every product" and the bit about apple pro earbuds was interesting plug for apple..... Steve Brrightfield did a fantastic job delivering confidence and conviction and got love the gadget guy when he says its just amazing where this technology is going, i guarantee Brainchip will be involved in same ways shape or form. Got love the gadget professor he seems to have the ability to draw out info, I remember when he interviewed Rob Telson, he took off his headset and fiddled with his hearing aid and Rob smirked, almost like a wink wink nudge nudge and here we are today talking about hearing aids etc
And what's with the guy french guy that came on after calling his company Akidaia ? I thought the word akida was trademarked to us? bit confused what that was all about with the name.
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Thanks Slade.......that in my opinion was the best 2 way conversation I have heard, second only to Peter or Anil !
If any shareholder has any doubt about Brainchip, well listen very carefully to little parts of the conversation, Steve drops
a few bits of information, including mentioning but obviously not naming 2 new companies that we are currently engaged
with....the Professor asked some great lead questions and was clearly truly blown away with the "science fiction", he is also
the second one to mention Branchip's professionalism in returning phone calls and making ourselves available in an as open
manner as possible.
Clearly shareholders can see that Steve is not only very knowledgeable, but has a great demeanor, what a great addition to
our team in the US.
On reflection last night, I wish to withdraw my choice of word, as in, FANTASY......I would love to see the entire Brainchip Team
ringing the opening bell at the Nasdaq, with Peter and Anil front and centre, but my heart tells me a bigger entity will buy us out
before that day eventuates.....remember I'm wrong (sometimes) and it's only my (opinion).
Love our company and all our genuine supporters
Tech.P.S. Remember.......
jtardif999
Regular
Yeah and we are still potentially part of the Nanose gig: (part of claim 65 anyway)
‘a processing unit comprising a learning and pattern recognition analyzer configured for receiving output signals from the one or more of the sensing regions and comparing the signals to a stored data, by utilizing a pattern recognition algorithm;’
I liked the continuation of that where they mentioned something about a company having their own university program to retrain their staff for it.Cheer Slade.
That's is some gold nuggets
Not the exact qoute but, I like it.
'We sign more partners, where we can't mention.
Learning
Stable Genius
Regular
Mercedes-Benz expands open innovation activities in Canada alongside research in neuromorphic computing. | Mercedes-Benz Group
Mercedes-Benz and the Ontario government through the Ontario Vehicle Innovation Network (OVIN) establish incubators to foster startup creation, startup scouting and automotive innovation in Ontario, Canada. The OVIN Incubators will join the growing international Mercedes-Benz STARTUP AUTOBAHN...
Tothemoon24
Top 20
TENNs-PLEIADES: Building Temporal Kernels with Orthogonal Polynomials
Introducing TENNs-PLEIADES (PoLynomial Expansion in Adaptive Distributed Event-based Systems), an innovative method within the TENNs (Temporal Neural Network) family.
TENNs-PLEIADES excels in directly performing spatio-temporal classification on data from event-based sensors. These sensors offer ultra low-latency, and until now, processing their signals in a principled manner has been a significant challenge. By utilizing a unique kernel parameterization based on orthogonal polynomials, TENNs-PLEIADES achieves state-of-the-art performance across three distinct datasets: Hand Gesture Recognition, Eye Tracking, and PROPHESEE’s 1 Megapixel Automotive Detection.
TENNs-PLEIADES is fully supported on Akida 2.0 IP and represents the latest advancement in event-based and neuromorphic processing.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Oh, thank goodness! I was wondering when we were going to get around to leveraging our orthogonal polynomials to parameterize temporal convolutional kernels.
Do you know what this is about @Tothemoon24 Or are you posting because someone new is following?
Tothemoon24
Top 20
Absolutely no idea @Bravo well above my $2.19 pay rate , must say some of the big words look impressiveOh, thank goodness! I was wondering when we were going to get around to leveraging our orthogonal polynomials to parameterize temporal convolutional kernels.
Do you know what this is about @Tothemoon24?
Tothemoon24
Top 20
I struggle with opening my Kellogg’s Coco PopsWhat?!
Do you mean to say you didn’t utilize your unique kernel parameterization for breakfast this morning? That’s very disappointing.
Boab
I wish I could paint like Vincent
From memory it has been on the website for quite a while as a "white Paper".
But, like you I have no idea what it all means.
stockduck
Regular
"
Edge computing has naturally been a hot topic at CES with companies highlighting a myriad of use cases where the pre-trained edge device runs inference locally to produce the desired output, never once interacting with the cloud. The complexity of these nodes has grown to not only include multimodal support with the fusion and collaboration between sensors for context-aware devices but also multiple cores to ratchet up the compute power.
Naturally, any hardware acceleration has become desirable with embedded engineers craving solutions that ease the design and development burden. The solutions vary where many veer towards developing applications with servers in the cloud that are then virtualized or containerized to run at the edge. Ultimately, there is no one-size-fits-all solution for any edge compute application.
It is clear that support for some kind of hardware acceleration has become paramount for success in breaking into the intelligent embedded edge. Company approaches to the problem run the full gamut from hardware accelerated MCUs with abundant software support and reference code, to an embedded NPU.
...."
Can someone more knowledgeable show the data inside this link I posted? So many NPU`s... Where is our akida "gost" IP?
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Bravo
If ARM was an arm, BRN would be its biceps💪!
Oh dear! Maybe you and Tothemoon should meet up with each other? I’m sure with your two heads combined, you could open Tothemoon’s packet of Coco Pops.
stockduck
Regular
I read an article about a spoon that can measure the salt content in food to support health at CES 2025.
"
Edge computing has naturally been a hot topic at CES with companies highlighting a myriad of use cases where the pre-trained edge device runs inference locally to produce the desired output, never once interacting with the cloud. The complexity of these nodes has grown to not only include multimodal support with the fusion and collaboration between sensors for context-aware devices but also multiple cores to ratchet up the compute power.
Naturally, any hardware acceleration has become desirable with embedded engineers craving solutions that ease the design and development burden. The solutions vary where many veer towards developing applications with servers in the cloud that are then virtualized or containerized to run at the edge. Ultimately, there is no one-size-fits-all solution for any edge compute application.
It is clear that support for some kind of hardware acceleration has become paramount for success in breaking into the intelligent embedded edge. Company approaches to the problem run the full gamut from hardware accelerated MCUs with abundant software support and reference code, to an embedded NPU.
...."
Can someone more knowledgeable show the data inside this link I posted? So many NPU`s... Where is our akida "gost" IP?
How cool would it be to develop a metal needle that can measure the acrylamide content of food, such as French fries or similar, in order to reduce the risk of cancer when consuming food... due to incorrectly prepared products in stores?
Just a quick question for our group here....
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