Flow's PPU turbocharges general purpose computing of SuperCPUs for the most demanding applications across different device categories.

Next generation SuperCPUs will change our devices as we know them

What is possible with current devices as we know them will be fundamentally changed with Flow powered SuperCPUs. The limits of what can be done will be pushed further by enabling more and more demanding applications. 

Smartphone users can have locally hosted AI in their pockets. XR/VR devices will not just show a reality that looks like the real world, but also to behaves and feels like it. Laptops and PCs will will become artificial general intelligence machines. 

When the future of the general purpose computing is no longer limited by its current sequential nature and lurking limitations of geometry, new innovations in devices categories and their hybrids can emerge. 

Enter the era of the SuperCPUs, enabled by Flow's technology.

Flow's groundbreaking PPU turbocharges embedded systems for uses such as edge and cloud computing, AI clouds, multimedia codecs across 5G/6G, autonomous vehicle systems, military-grade computing and more.

Performance for
the era of Artificial
/ AI


Artificial Intelligence with Flow-powered CPUs

CPU and its general-purpose computing is and will remain a critical portion of numerous AI workloads. Analytics, information retrieval, and ML training and serving all require a huge amount of computing power. 

All parties wishing to maximize performance, reduce infrastructure costs, and meet sustainability goals have run against a slowing rate of CPU improvement. Unless CPUs can keep up, general purpose computing will limit the capability and dominate the cost of AI.

Where SuperCPU can help save time and cost in AI?

Pre and post-processing of data currently chews up a lot of time of the total time involved in teaching an LLM a new language. We have proven that this can be significantly reduced with PPU-powered CPUs. What’s more, Flow's PPU architecture will finally make locally-hosted AI a reality.


Faster edge computing and local in-vehicle computing boost are a must for autonomous vehicles

Autonomous vehicles generate massive amounts of data from on-board sensors — cameras, LiDAR, radar, and ultrasonic sensors. It needs to be processed in real-time to allow the vehicle to make split-second driving decisions.
Edge and in-vehicle computing addresses the challenge by processing data in the vehicle or data centers close to the vehicle. The result is much faster data processing with significantly lower latency. For a self-driving car traveling at high speeds, reducing latency from over 100 milliseconds in the cloud to under 10 milliseconds on the edge can mean the difference between a safe stop or a collision.

Integrating Flow Parallel Processing Unit (PPU) into this ecosystem dramatically amplifies the vehicle's capability to handle data-intensive operations. The PPU architecture is specifically designed to accelerate parallel processing tasks – ideal for efficiently managing simultaneous inputs from multiple sensors.

With Flow PPUs, autonomous vehicles can achieve vastly faster processing speeds, further reducing latency to ensure instantaneous responses to environmental changes. This is crucial for object recognition, scenario analysis, and decision-making processes – enabling vehicles not only to react to imminent dangers but also to anticipate potential hazards before they occur.

Flow will enable a fundamental change in how future industries perform.


Ramping up the speed of innovation

Fields such as business computing, logistics planning and investment forecasting will greatly benefit from Flow technology through numeric and combinatoric simulation and optimization. Flow PPUs bring vastly enhanced control flow management over what traditional, heavily parallelized, GPU cluster-based systems could ever provide.
Flow technology is equally powerful for classic numeric and non-numeric parallelizable workloads, including matrix and vector computations as well as algorithm sorting. Even if your code contains small parallelizable segments that have yet to be parallelized due to high runtime overheads, Flow's PPU will still provide a significant performance boost and optimize efficiency across your computing tasks.

In defense, whoever processes the data the fastest will win. Here, missiles and drones, missile and drone defense, and military aviation are the most attractive use cases — and Flow contributes to all these for a major geopolitical impact.

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