[frankinstien]

Since I believe the neural network approach isn't the most efficient use of digital resources because it effectively is a linear search in a problem or knowledge domain, no matter how much pruning of a network you do. So I'm using a good deal of RAM, 128GB, and two 10 core Xeons. This doesn't mean I don't use GPUs, I do and its a Vega 56, I use it primarily to work with fuzzy logic and fast index calculations as well as process video and audio data. Below is a diagram of how I organize memory from RAM, SSD to hard drives.



The SSD is a 1 TB drive and it stores data from a noSql database I engineered that uses O(1) lookup indexing. The Hybrid hard drive is more of a backup of the SSD but will store very large datasets like long video. You'll notice a block called "Cross Domain Harness" I really wanted to avoid a lot of serializing and deserializing between processes, so the harness allows the dynamic injection of various programs under a single domain. Since when I prototype I haven't necessarily integrated all the pieces yet and I don't want to deal with the latency of serialization. This way I can pass data by reference so long as the programs have imports to the datatypes used. It makes life a lot easier to troubleshoot particularly when the amount of data loading is in the 10s of GBs.

The machine was built three years ago so upgrading is starting to seem like a good idea since AMD's processors are reasonably priced, however, the RAM prices haven't dropped by much. So if I did upgrade it's a 50% improvement in performance using the Ryzen 9 3900X 12-core. Because my current machine is a dual-socket Xeon board it has 16 memory slots which allows me to use lower-cost 8GB sticks. In fact, I do debate if it's worth putting up the money for the newer Ryzen or just getting 16GB sticks to work with larger datasets?

[infurl]

That's a pretty good setup that you've got there. I'd hold off on upgrading your hardware until you can at least double your performance. In the meantime, work on improving your algorithms or implementation because ultimately that's where the most gains are to be made. When it's too easy to keep getting faster hardware, it's easy to forget that.

What exactly can you do with it at the moment? You can store an awful lot of data, is there any particular problem that you are trying to solve?

[frankinstien]

What exactly can you do with it at the moment? You can store an awful lot of data, is there any particular problem that you are trying to solve?

The objective is to build the infrastructure for episodic memories and process and store information across a spectrum of sensory inputs, so it can be freely associated, yet be very consumable for digital processing.

[Yervelcome]

Do you have a write up where I can read about it?

[frankinstien]

Do you have a write up where I can read about it?

I don't have any formal material fully explaining the concepts of software right now but it's a "To Do" in the near future.

[infurl]

Do you have a write up where I can read about it?

I don't have any formal material fully explaining the concepts of software right now but it's a "To Do" in the near future.

Since you've piqued our interest I daresay the coming barrage of questions will prompt you to make a start very soon. 

I'm curious about the custom solution that you're developing. You described it as noSQL and having O(1) access characteristics which implies hashing. You said you were using your GPU for indexing which I understand to mean that you are using it for generating your hash keys. I can guess why you would want to do that if you're interested in turning media like video into associative memory.

How about the symbolic side of things. Have you done anything with relational databases or triple stores such as Resource Description Framework (RDF)? What about ontologies?

[frankinstien]

I'm curious about the custom solution that you're developing. You described it as noSQL and having O(1) access characteristics which implies hashing. You said you were using your GPU for indexing which I understand to mean that you are using it for generating your hash keys. I can guess why you would want to do that if you're interested in turning media like video into associative memory.

Yes hashing is used but it's a bit more exotic than other applications since it has to work with fuzzy sets of data. For the video, the approach is breaking visual data into manageable pieces that realize into generalizations where those generalizations allow for associations.

How about the symbolic side of things. Have you done anything with relational databases or triple stores such as Resource Description Framework (RDF)? What about ontologies?

The ontological framework is custom as well and uses NoSql for long term storage and is structurally a graph database. The entire approach is object-oriented and allows for sperate threads to crawl through knowledge domains as well as allowing for updates to propagate through the graph instantly. It was inspired by Roget's Thesaurus. It's used to evaluate speech or text where that data can be analyzed as to what kind of bias generalizations can be derived from the hierarchal nodes. Words are endowed with properties and allow for validation of a word's use within a sentence. This helps in quantifying logic, context, and meaning of a sentence or groups of sentences.

[infurl]

Yes hashing is used but it's a bit more exotic than other applications since it has to work with fuzzy sets of data. For the video, the approach is breaking visual data into manageable pieces that realize into generalizations where those generalizations allow for associations.

How does that differ from say fingerprinting a photo or music to find duplicates, or Google's ability to identify copyright material in YouTube videos?

The ontological framework is custom as well and uses NoSql for long term storage and is structurally a graph database. The entire approach is object-oriented and allows for separate threads to crawl through knowledge domains as well as allowing for updates to propagate through the graph instantly. It was inspired by Roget's Thesaurus. It's used to evaluate speech or text where that data can be analyzed as to what kind of bias generalizations can be derived from the hierarchical nodes. Words are endowed with properties and allow for validation of a word's use within a sentence. This helps in quantifying logic, context, and meaning of a sentence or groups of sentences.

It sounds like you are doing more than just sentiment analysis then. Do you have some examples of inputs and outputs to illustrate what you can use it for?

[frankinstien]

How does that differ from say fingerprinting a photo or music to find duplicates, or Google's ability to identify copyright material in YouTube videos?

I'm not sure what Google is doing to uniquely identify visual images to protect copyrights, but the method I'm using is based on fractal dimensions.

It sounds like you are doing more than just sentiment analysis then. Do you have some examples of inputs and outputs to illustrate what you can use it for?

I will be putting together some examples and videos for my website and I'll post them on this forum as well.

[frankinstien]

Well, I've waited and waited and the chip shortage continues.  So, I coughed up some cash and have upgraded my HAL rig to a Ryzen Threadripper 3960x 24 cores 48 threads 3.8ghz with 256 GB of ram at 2133 MHZ. Also added a 2TB SSD NVMe PCIe that can do R/W up to 3,400/3,000 MB/s. I'm sticking with my Vega 56 for now. So this rig is three times faster than the older one with the current GPU, change the GPU to a NVIDIA GeForce RTX 3090 24GB and I'm 6 times faster.

Oh and this rig is actually illegal in California as a PC, but as a server it's legal. Welcome to my world! 

[WriterOfMinds]

I don't know that we even got through the crypto mining craze before this chip shortage started plaguing us    When I built my last PC, I went to the local parts store and whole sections of shelf in the GPU area were bare.

Oh and this rig is actually illegal in California as a PC, but as a server it's legal.

What's up with that? Are they trying to limit electricity consumption?

[frankinstien]

What's up with that? Are they trying to limit electricity consumption?

That's exactly why. Dell won't ship some Alienware rigs to California, Colorado, and other green states for the same reason. Gamming is going to suck in those states for a while.

[frankinstien]

I added an additional NVMe Internal SSD - Gen4 PCIe, M.2 2280, 3D NAND to the rig. The biggest problem I face with storage is serialization and deserialization which is why I use soo much ram. When its just ram I can simply reference an object's memory location, so I can build very intricate indexes that reference the memory location and not the object itself. If you try and store those references in a drive it will make redundant copies of the object itself because the memory location of the object in ram will not be relevant if and when you reload the object.  So I replace the references with an ID of the object in a OO database. But Intel has Optane DDR4s! literally, solid-state storage in a Dimm form factor that plugs into a ram slot like SDRAM! The Optane Storage technology has speeds of 170 to 360 nanoseconds compared to DRAM which operates at around 90 nanoseconds, but it's still pretty fast compared to the fastest SSDs. With this kind of concept, I could persist the memory references as if they were in RAM and not have to map from object IDs to actual objects that are deserialized from disk storage. Yes, those Optane DDR4 persistent memory modules store data even when the machine is powered off!

Unfortunately, these gems will only work with the latest Intel machines where the core densities and speed are nowhere near AMDs CPUs at the same price.   
I found a 256GB DDR4 Optane module on eBay for $400!

Here's a video that explains it in a real implementation:

[frankinstien]

Here's another video on the Optane DDR4 technology. It turns out that Micron has sold the fabrication facility that makes Optane to Texas instruments who will not be using it to make Optane type chips for the DDR4 form factor or any other app inclusive of SSDs, however, Intel no longer will sell Optane SSDs either. Intel will be relying on its Rio Rancho, New Mexico, facility for Optane chips for the DDR4 form factor. So Micron is out of the 3D XPoint business, but Intel and Micron still retain the intellectual property of 3D XPoint. Maybe Micro will find a buyer for the 3D XPoint IP or maybe CXL will deliver something even more amazing.  It would be nice if there could be some bio update for xTRS4 motherboards to use the DDR4 persistent data modules, the ability to read storage like ram and not have to serialize and deserialize objects but simply reference them saves tons of CPU cycles.

[MagnusWootton]

Looks like you want to implement something like hard disc streamed texture-mapping, for a bigger memory store.    From my experience I found hard disc reads were 20x faster than hard disc writes,   writing is even worse!    So if you kept the writes down it would go faster,  but still the reads are only slowish too, but writing is even worse.

I had a cellular automata thing happening off the hard disc,  and thats what I found.
(Learning/writing is REALLY slow,   but recall is not as bad.)