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Mythos (MemorY To High Operational Speed) is the result of a research started to give answers to some issues raised in the DARPA HyDDENN (Hyper-Dimensional Data Enabled Neural Networks) program. Mythos™ can work with Hyper-Dimensional Data by using less than log2(parameters) for each learning and inference operations. |
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Hyper-Dimensional Data Enabled Neural Network |
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· Running ML on Von Neumann CPU |
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· Statistical Consistency Selective Availability |
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· Constant time for Learning and Inference |
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· Continuous Learning ( Plasticity + Stability ) |
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· Huge Datasets Management |
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· Hardware Adaptive Technology |
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· t = log2 ( parameters ) |
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· No MAC operations |
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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. |
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WHO_WE_ARE WHAT_WE_DO TECHNOLOGY General Synaptics Aerospace_&_Defence_Machine_Learning_Company VAT NUMBER:_IT0267070992 REA NUMBER: GE-503104 Email:_luca.marchese@synaptics.org |
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