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Most recent issue published online in the International Journal of Critical Computer-Based Systems.
International Journal of Critical Computer-Based Systems
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Mathematical processing units: a prototype for enabling next-generation ambient AI
The next generation of AI spanning from large language models (LLMs) to joint embedding predictive architectures (JEPAs) depend heavily on high-quality ambient data acquisition. However, the effectiveness of these future models is directly tied to the fidelity and efficiency of their training data pipelines. We present a proof-of-concept for a new type of processing hardware called mathematical processing units (MPUs), built upon commercially available FPGAs and designed to acquire and pre-process ambient sensor data with high fidelity, low power, and low cost, while also aiding in sensor fusion and fault tolerance. We demonstrate the effectiveness of the proposed approach by performing Kalman filtering on environmental data. Across six sensing scenarios, the MPU-based Kalman filter implementation reduced measurement noise by an average of 77.2%, demonstrating substantial improvement in signal fidelity. These results highlight the potential of MPUs as core infrastructure for future AI systems that interact with the physical world.