Machine Learning

The Atrevido core, with its Out-of-Order scheduling engine combined with our Gazzillion technology, can deal with highly sparse data, with long memory latencies and with high-bandwidth memory systems, typical of current machine learning applications.

Key-Value Stores

In-memory caching and key-value processing requires large numbers of outstanding transactions to your memory subsystem.

Our Gazzillion Misses™ technology is especially suited for these demands, allowing you to sustain full memory bandwidth with fewer processing cores.

Recommendation Systems

The Gazzillion technology is specifically designed for Recommendation Systems, a key part of DataCenter Machine Learning.

By supporting hundreds of misses per Atrevido, you can build an SoC that smoothly delivers highly sparse data to the computer engines without a large silicon investment.

Furthermore, the 2-way Out-of-Order core helps to accelerate the not-so-parallel portions of Recommendation Systems.

Sparse Data/HPC

Gromacs? Sparse Matrices? PDEs? OMP? MPI? Multi-level parallelism? You name it!

HPC has to deal with large, multi-dimensional, sparse data and support heterogeneous parallelism at multiple levels.

Atrevido, with Out-of-Order execution and Gazzillion Misses™ technology, is the right match for the HPC domain, be it Embedded HPC, Edge or DataCenter level.