Just checked. He’s gone from LinkedIn. WTF.
Here we go, I came across this:
"About Rob Telson
Rob Telson is a Sales and Channel Leadership. GTM and Business Development. Global and Regional Experience at Wavious based in San Diego, California.
Previously, Rob was a Vice President, Ecosystem & Partnerships at BrainChip."
Not sure how valid this source is, but sad to see him leave Brainchip if this is the case![]()
Maybe Rob did not like the slow going with the CEO of Brainchip mr hairline
YES, still thereWhat about X? Is he still there? Did he post something there?I have no x account
Are you really talking about Rob Telson? (I have not read the following pages yet)
He is still on LinkedIn https://www.linkedin.com/in/rob-telson-8153a51/
According to Rob Telson’s LinkedIn profile, he does have ties to Wavious, though - he used to be a strategic advisor to them for more than five years, while working for Synopsis, Cylynt and BrainChip.
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Rob Telson - CELUS | LinkedIn
Experienced sales leader with a demonstrated successful history working in the software… · Experience: CELUS · Education: Harvard Business School · Location: Irvine · 500+ connections on LinkedIn. View Rob Telson’s profile on LinkedIn, a professional community of 1 billion members.www.linkedin.com
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Not sure I understand the relevance of a problem with an analog neuron and Akida's digital architecture.I usually post interesting (but also ridiculous) stuff from TSE in the German forum. This time however, the user “perhaps” who also is sometimes here active, postet today something interesting, I will share.
“I've taken a closer look at MYWAI because I see a lot of potential there. The MYWAI projects use a novel neuromorphic memristor technology (computing processes executed in memory) from General Vision.
If everything works as they claim, they have made a breakthrough in one of the biggest problems in neuromorphic computing. When the task changes, an automatic adjustment of the required neurons is necessary; otherwise, there will be increased inaccuracies. So far, there has been no solution to this problem. Akida is also affected by this issue. With General Vision's approach, it is theoretically possible to control the neurons of the Akida processor via the memristor, unlocking the full potential of Akida. Therefore, the progress of the MYWAI projects deserves increased attention, as this could be where the big breakthrough happens.
Sources: https://general-vision.com/download/neuromem-technology-reference-guide/?wpdmdl=12284&refresh=660275755e5f11711437173 https://neurotechnologijos.com/zusammenarbeit/?lang=de https://www.myw.ai/projects “
So far, the lack of an interface to implement the technology and make it mass-market compatible is the problem. That's why we are constantly hovering in the field of "testing" and quasi-successful integration of Akida into studies and prototypes, etc. It's about mass marketability.Not sure I understand the relevance of a problem with an analog neuron and Akida's digital architecture.
Never seen someone self describe as a polymath.View attachment 59896
Nvidia’s H100 AI #GPUs are taking the tech world by storm, but their reign comes at the price of a hefty energy bill.
According to a report from #CBInsights and #Stocklytics.com, these power-hungry processors are projected to consume a staggering 13,797 GWh in 2024, exceeding the annual energy consumption of nations like #Georgia and #CostaRica.
Imagine this, a legacy data center consumes 10 kW/rack where #CyrusOne, #KKR owned leading global data center operator and developer specialising in #AI applications, consumes 300 kW/rack!
But why do #GPUs consume so much power?
Data center #GPUs consume a substantial amount of power primarily due to their high computational requirements and the complex algorithms they handle. These #GPUs optimize parallel processing tasks like #machinelearning and #dataanalytics, involving simultaneous processing of vast amounts of data.
While #parallel processing speeds up data processing, one demerit is that, at a time most parts of a chip are active. This constant computation, coupled with the execution of complex algorithms, demands significant computational power, thereby increasing energy consumption.
The large-scale deployment of #GPUs in data centers, where racks and clusters utilize hundreds or thousands of #GPUs further amplify their collective power consumption. This combination of factors underscores the considerable energy consumption associated with data center GPUs.
Successfully navigating these challenges and fostering innovation will shape the future landscape of #AI computing.
So, what options do we have?
● #Amazon, frenemy to Nvidia, recently unveiled Arm based Graviton4 and Trainium2 chips holds promise for efficiency gains.
● In the near to medium term, #Neuromorphic computing is being researched aggressively as an alternative to synchronous parallel computing architectures. Neuromorphic computing is an asynchronous computing paradigm which runs on event based ‘spikes’ rather than a clock signal. And drastically lowers the power consumption.
● Big money is going into enabling tech like liquid cooling - #KKR acquired CoolIT Systems for $270 mn and Bosch acquired Jetcool through its venture arm
While CooIT Systems becomes the supplier for Cyrus One, #KKR makes money on both!
Good work Frangipani.As others have remarked earlier, Wavious seems to be out of business and their website is no longer accessible.
Apparently, their former CEO Benny Malek recently joined the Board of Endura Technologies:
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Intriguingly, quite a number of previous Wavious employees (all with a Qualcomm background before joining Wavious, by the way, just like former CEO Benny Malek and former CTO Hanan Cohen) have since ended up at Apple - more than a coincidence? A quick Google search didn’t come up with any evidence of a takeover/acquisition of Wavious by the Cupertino-headquartered tech giant, though.
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Not finding any patents for General Vision (GV), but I found this one for Norlitech:I usually post interesting (but also ridiculous) stuff from TSE in the German forum. This time however, the user “perhaps” who also is sometimes here active, postet today something interesting, I will share.
“I've taken a closer look at MYWAI because I see a lot of potential there. The MYWAI projects use a novel neuromorphic memristor technology (computing processes executed in memory) from General Vision.
If everything works as they claim, they have made a breakthrough in one of the biggest problems in neuromorphic computing. When the task changes, an automatic adjustment of the required neurons is necessary; otherwise, there will be increased inaccuracies. So far, there has been no solution to this problem. Akida is also affected by this issue. With General Vision's approach, it is theoretically possible to control the neurons of the Akida processor via the memristor, unlocking the full potential of Akida. Therefore, the progress of the MYWAI projects deserves increased attention, as this could be where the big breakthrough happens.
Sources: https://general-vision.com/download/neuromem-technology-reference-guide/?wpdmdl=12284&refresh=660275755e5f11711437173 https://neurotechnologijos.com/zusammenarbeit/?lang=de https://www.myw.ai/projects “
You might be interested in this GV presentation I just found which includes your Norlitech so good sleuthing D.Not finding any patents for General Vision (GV), but I found this one for Norlitech:
US2020082241A1 COGNITIVE STORAGE DEVICE 20180911
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a system comprising a non-volatile storage memory, a controller, and a cognitive memory. The storage memory can store data. During operation, the controller programs a function for the system based on a configuration file. The function indicates one or more operations for the data stored in the storage memory. The cognitive memory can include a set of neuron memory cells, which can store a knowledge base for facilitating the function and execute a pattern matching operation between the data stored in the storage memory and the data stored in the set of neuron memory cells. The controller can then execute the one or more operations within the system based on an output of the pattern matching operation from the cognitive memory.
[0052] FIG. 1B illustrates an exemplary architecture of a CSD, in accordance with an embodiment of the present application. Search engine 130 can include a programmable hardware module 170 , which can be a configurable piece of hardware capable of accessing storage memory 150 , at least in part, to search for reference patterns loaded in cognitive memory 158 . Module 170 can execute firmware-level codes, and operate as a interface logic between storage memory 150 , cognitive memory 158 , cache 156 , and the host (i.e., storage node 116 ). In some embodiments, module 170 can be an FPGA-based module coupled to cognitive memory 158 . Module 170 can be based on one or more of: integrated circuitry, and a semiconductor intellectual property (IP) core. Cognitive memory 158 can include a neuron-based integrated circuit and/or a semiconductor IP core arranged as a single memory bank or a plurality of neuron banks 172 , 174 , and 176 . The components of cognitive memory 158 may be coupled via a PCB, or on multiple chips. Module 170 and cognitive memory 158 can also be parts of an integrated circuit on a common substrate (e.g., on the same die).
What makes this relevant to GV is the inventors are listed as GV employees:
PAILLET GUY; MENENDEZ ANNE
Looks pretty clunky.