Although Moore’s Law has slowed at bit as chip makers reach the physical limits of transistor size, researchers are having to look to other things other than cramming more transistors on a chip to increase CPU performance. ARM is having a bit of a moment by improving the performance-per-watt of many computing platforms, but some other ideas need to come to the forefront to make any big pushes in this area. This startup called Flow Computing claims it can improve modern CPUs by a significant amount with a slight change to their standard architecture.

It hopes to make these improvements by adding a parallel processing unit, which they call the “back end” to a more-or-less standard CPU, the “front end”. These two computing units would be on the same chip, with a shared bus allowing them to communicate extremely quickly with the front end able to rapidly offload tasks to the back end that are more inclined for parallel processing. Since the front end maintains essentially the same components as a modern CPU, the startup hopes to maintain backwards compatibility with existing software while allowing developers to optimize for use of the new parallel computing unit when needed.

While we’ll take a step back and refrain from claiming this is the future of computing until we see some results and maybe a prototype or two, the idea does show some promise and is similar to some ARM computers which have multiple cores optimized for different tasks, or other computers which offload non-graphics tasks to a GPU which is more optimized for processing parallel tasks. Even the Raspberry Pi is starting to take advantage of external GPUs for tasks like these.

When something does zero-shot image classification, that means it’s able to make judgments about the contents of an image without the user needing to train the system beforehand on what to look for. Watch it in action with this online demo, which uses WebGPU to implement CLIP (Contrastive Language–Image Pre-training) running in one’s browser, using the input from an attached camera.

By giving the program some natural language visual concept labels (such as ‘person’ or ‘cat’) that fit a hypothetical template for the image content, the system will output — in real-time — its judgement on the appropriateness of such labels to what the camera sees. Again, all of this runs locally.

It’s maybe a little bit unintuitive, but what’s happening in the demo is that the system is deciding which of the user-provided labels (“a photo of a cat” vs “a photo of a bald man”, for example) is most appropriate to what the camera sees. The more a particular label is judged a good fit for the image, the higher the number beside it.

This kind of process benefits greatly from shoveling the hard parts of the computation onto compatible graphics cards, which is exactly what WebGPU provides by allowing the browser access to a local GPU. WebGPU is relatively recent, but we’ve already seen it used to run LLMs (Large Language Models) directly in the browser.

Wondering what makes GPUs so very useful for AI-type applications? It’s all about their ability to work with enormous amounts of data very quickly.

Learn how to use WebGPU compute shaders to create reaction-diffusion patterns.

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