M1076 Analog Matrix Processor
mythic.ai
mythic.ai
“Mythic…ran out of cash last year and was nearly forced to halt operations…it managed to bring in a relatively modest $13 million investment several months later in March” (2023)
https://www.reuters./technology/nvidias-dominance-ai-chips-d...
Hopefully they have some kind of revenue coming in because it really looks like they're doing some interesting work.
Fabs no longer think of design creators as their customers. They think of design creators as their portfolio companies. The fab invests in them: not cash, but rather production capacity. Unless you are Apple/Nvidia/Broadcom/Qualcomm-sized, the fabs do not consider you to be a customer. They will pressure you, hard, to take on a "design partner" like Broadcom, and to become that company's customer. The fabs are not particularly interested in having you pay them. At best they might be interested in having you pay the bigger fish (GUC, Synopsys, etc) who are the fab's true customers.
Having the fabs behave like equity investors has, unfortunately, crowded out all of the real equity investors.
Of course, the organic neural networks in our brains have all these issues and more and still do fine. On the other hand, our brains have billions of years of evolution that results in them being fault tolerant in ways that this chip can replicate at all.
Your question still stands, however. And for an analog chip it's basically the question of how to qualify, validate, and characterize neural networks in general. I'm not convinced we have the faintest idea how to do that in any formal sense.
This chip is said to perform 25 TOPS. A A100 80GB SXM is said to perform 1248 TOPS. Which is an entire card not a single CPU.
You could theoretically achieve the same with 50 M1076 processors.
Would the benefit be any of:
* a lot cheaper * more energy efficient. * small size * easier to mass produce?
They say that their chip only consumes about 10% of power compared to a digital GPU of similar computational capabilities.
- https://mythic.ai/wp-content/uploads/2022/02/MythicWhitepape...
> When a charge is programmed into a flash memory device, its electric field has an effect on any signal passing through it. In the Mythic architecture, the flash transistor acts as a variable resistor that reduces the signal level passing to the output. That reduction is proportional to the analog value stored in the memory. This simple effect implements the multiplication stage found in DNN calculations. The accumulation process, in which the output from each of those calculations is summed, is handled by aggregating the output of an entire column of memory cells. Thanks to these two properties, the Mythic architecture can process an entire input vector in a single step rather than iterating at high speed as in a digital processor
> Analog computing provides the ultimate compute-in-memory processing element. The term compute-in-memory is used very broadly and can mean many things. Our analog compute takes compute-in-memory to an extreme, where we compute directly inside the memory array itself. This is possible by using the memory elements as tunable resistors, supplying the inputs as voltages, and collecting the outputs as currents. We use analog computing for our core neural network matrix operations, where we are multiplying an input vector by a weight matrix.
> Analog computing provides several key advantages. First, it is amazingly efficient; it eliminates memory movement for the neural network weights since they are used in place as resistors. Second, it is high performance; there are hundreds of thousands of multiply-accumulate operations occurring in parallel when we perform one of these vector operations. Given these two properties, analog computing is the core of our high-performance yet highly-efficient system.
Training might be harder to implement.