Who_we_are

What_we_do

Technology

Customers

Partners

 

·       Running ML on Von Neumann CPU

·       Statistical Consistency Selective Availability

·       Constant time for Learning and Inference

·       Continuous Learning ( Plasticity + Stability )

·       Huge Datasets Management

·       Hardware Adaptive Technology

·       t = log2 ( parameters )

 

 

Mythos™ is a technology designed to obtain SIMD (Single Instruction Multiple Data) parallelism on a Von Neumann machine, for Pattern Recognition problems. Although the current processors are "Multi-Core", they cannot be compared to SIMD / MIMD (Multiple Instructions Multiple Data) processors, especially due to the limited number of cores, and are still considered as Von Neumann machines. For this reason there are GPU coprocessors that serve to have massive parallelism of the SIMD and MIMD type. In many cases it is not possible or not desirable to use parallel coprocessors: the algorithms of Mythos™ technology are designed for Pattern Recognition applications with the aim of increasing the performance on Von Neumann machines by orders of magnitude when a SIMD parallelism would be required by the application. In particular, the Mythos™ technology is oriented towards Pattern Recognition applications in the Aerospace sector, where Radiation-Hardened processors / SBCs such as the RAD750® produced by BAE Systems must be used. RAD750® SBC is the workhorse of the space industry, powering more than 100 satellites that carry out a variety of space missions. Radiation-Hardened processors must operate at lower cock frequencies than those typically used by processors operating in sectors that do not have this criticality.

The need for execution speed in algorithms seems now forgotten due to the widespread use of parallel SIMD and / or MIMD processors such as high clock frequency GPUs. In reality, there are sectors, such as aerospace, in which high clock frequencies cannot be used and given that Radiation-Hardened GPUs have not yet been implemented, the research on algorithms that allow to speed up Pattern Recognition problems by orders of magnitude becomes a current need.

 

 

 

 ©2024_Luca_Marchese_All_Rights_Reserved

 

 

 

Aerospace_&_Defence_Machine_Learning_Company

VAT:_IT0267070992

NATO_CAGE_CODE:_AK845

Email:_luca.marchese@synaptics.org

Contacts_and_Social_Media