Algebraic Machine Learning (AML) offers new opportunities in terms of transparency and control. However, that comes along with many challenges regarding software and hardware implementations. To understand the hardware needs of this new method it is essential to analyze the algorithm and its computational complexity. With this understanding, the final goal of this microproject is to investigate the feasibility of various hardware options particularly in-memory processing hardware acceleration for AML.


Simulation model for a PIM architecture using AML



Project Partners:

  • Algebraic AI S.L., Fernando Martin Maroto
  • Technische Universität Kaiserslautern (TUK), Christian Weis
  • German Research Centre for Artificial Intelligence (DFKI), Matthias Tschöpe

Primary Contact: FERNANDO MARTIN MAROTO, Algebraic AI

Main results of micro project:

We have carried out a theoretical study of the AML sparse crossing algorithm efficiency and identified in-memory processing and FPGA combined with in-memory processing as the two feasible options for Algebraic Machine Learning. Currently, we are working on a prototype implementation that involves FPGA and in-memory processing of bit arrays in commercial Upmem RAM memories.

Contribution to the objectives of HumaneAI-net WPs

This work is critical to speed up the calculation of Algebraic Machine Learning models and in so doing contribute to:
1- Bidirectional human-machine communication using formal expressions
2- Possibility to set goals and establish limits via formal constraints
3- Reduced dependency on statistics can help overcome bias
4- Transparency by design
5 -Possibility for decentralized, cooperative distributed machine learning

Tangible outputs

  • Program/code: AML engine prototype using bitarrays – Fernando Martin Maroto

Results Description

Sparse Crossing is a machine learning algorithm based on algebraic semantic embeddings. The goal of the collaboration is to first understand the needs and computational complexity of Sparse Crossing and then perform a feasibility analysis of various hardware options for an efficient implementation of the algorithm. Particularly, in-memory processing hardware acceleration and FPGA-based implementations have been considered.

A report, and a FPGA based prototype has been developed.


Fernando Martin-Maroto and Gonzalo G. de Polavieja. Algebraic Machine Learning.
arXiv:1803.05252, 2018.
Fernando Martin-Maroto and Gonzalo G. de Polavieja. Finite Atomized Semilattices.
arXiv:2102.08050, 2021.
Fernando Martin-Maroto and Gonzalo G. de Polavieja. Semantic Embeddings in Semilattices. arXiv:2205.12618, 2022

Links to Tangible results

The report and the details of the FPGA based prototype are available for review. Some results are under a patent process and are not yet available to the public.