The need for cheaper AI appears to have hit home, thanks to inflated bills in the pay-as-you-use models that AI Labs have moved towards in recent times. So, any opportunity to reduce cost of power consumption is being seen as an option worth exploring as is the case with Unconventional.AI, a company led by former AI head at Databricks.
The company, led by Naveen Rao, released its first AI model called Un-0 for image-generation that suggests for the first time that technology can replicate conventional AI systems at a much lower level of power consumption. The new system could ultimately reduce power use by as much as 1,000 times. Now, that would be music to the ears of OpenAI and Anthropic.
So, what exactly is the secret sauce? Unconventional.AI says inference processing becomes a vastly power efficiency process through the use of a new oscillator-based computer architecture. A paper released by the company details out the process of how an image generation model was built using a software simulation of the new architecture.
A blog written in early May lists out means to reduce the power consumption by 1000x. “For example, don’t fully specialize the hardware to the computation, but specialize more than a GPU currently does,” it says.
The idea is to design the hardware architecture to match an AI model’s architecture so that the data movement is minimized. For example, dataflow architectures can be spatially laid out in CMOS to minimize the distance that neuron activations need to traverse to reach the next layer of the neural network, which in turn can reduce energy consumption, it said.
Another option relates to an even more extreme than designing the AI hardware to map exactly to the AI model architecture one wishes to run on it, is to specialize to a specific trained instantiation of that model by hard-wiring the model parameters in the chip. This has been an approach adopted by Taalas and advocated for in academic papers.
A third several other ideas relates to parameter efficiency by reducing the total amount of memory required by trading off using fewer parameters and more computation with those parameters.
“This has been explored in the conventional ML community in work on deep equilibrium models and recursive models. Physical dynamical systems realized in electronic circuits can naturally incorporate the lessons from these approaches, and, in some cases, it is even more natural to implement them with continuous-time physical systems,” the blog says.
Meanwhile, founder Naveen Rao told TechCrunch that the company would, over the next year, start seeing some pretty interesting news around this. The model released by the company enhances performance using an oscillator-based architecture that vastly different from chips that power conventional computing and traditional LLMs, the report said.
For now, Un-0 runs on a software simulation of the company’s oscillator chips, but they are in advanced stage of planning the release of schematics from an actual chip soon. Thereafter, it would create an entire inference stack from ground-up with Unconventional.AI then providing compute capacity just any other provider.
“We will build a new kind of system composed of our chips. We will run AI models there, and we will have a network cable where prompts come in and inferences go out, but it’ll be done at 1/1000 of power,” Rao has told TechCrunch.
“The high-level approach we are taking is to develop both the AI models and the AI hardware from the ground up together – co-evolving their development so that the hardware operates as close to the limits of what is physically possible with current CMOS technology, and the AI models running on the hardware optimally match the hardware’s capabilities, the blog says.
Seems to be an interesting experiment, which for the sake of the AI ecosystem, we hope will do its bit to cut down user costs. As Rao says, AI scaling has been tough due to energy which could become the limiting factor in a few years. Which is why he is hoping that his solution could resolve the energy-limitation challenge. We promise to keep a close watch on how things go with Unconventional.AI in the short-term.
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