Presumono

Presumono is a distributed artificial‑intelligence framework designed to streamline the deployment of machine‑learning models across heterogeneous computing environments. The project was initiated in 2021 at the Institute for Computational Science at the University of Zurich by a team of researchers led by Dr. Lina Hofmann. According to the original white paper, the goal was to create a lightweight middleware layer that abstracts the complexities of data routing, task scheduling, and resource allocation while maintaining high throughput and low latency.

At its core, Presumono employs a task‑based programming model. Users define "presumptions"—high‑level declarative statements that capture the logical dependencies between data streams and computational kernels. The Presumono runtime automatically constructs directed acyclic graphs, performs dependency analysis, and distributes workload to available CPU, GPU, or FPGA devices. The system also includes a fault‑tolerance mechanism that retries failed tasks and can roll back to a consistent state without jeopardizing data integrity.

The framework is open source and distributed under the Apache 2.0 license. It is written primarily in Rust to take advantage of the language’s memory safety features, with critical algorithms implemented in C++ for performance. Presumono has been integrated into several research projects, notably in autonomous vehicle perception, where it enabled a 35 % reduction in inference latency compared with existing pipelines.

Future plans for the project involve expanding support for federated learning scenarios, adding a graphical user interface for monitoring distributed workloads, and extending compatibility with container orchestration tools such as Kubernetes. Nonetheless, Presumono remains a niche tool most commonly utilized within academic research labs and specialist industrial laboratories that require customized distributed inference solutions.