POMAA

A major problem in the use of

AI is the large demand for fast and efficient hardware. Current methods of

machine learning, especially deep neural networks, are generally based on

computationally complex numerical methods. The resulting computational effort

is particularly critical when AI or ML methods are to be used in miniaturized

and possibly embedded systems, since in this case both the permissible physical

size and the energy consumption must be as low as possible. A possible approach

here is the use of highly specialized hardware, e.g. in the form of an ASIC for

machine learning. Especially with these conflicting requirements - high

performance and accuracy on the one hand, low energy consumption on the other

hand - it is also necessary not only to use hardware optimized according to a

single criterion, but also to apply different criteria and to develop a

Pareto-optimal system with regard to different criteria. Optimally, such a

system offers not only ML-specific functionality, but also support of classical

software-specific functions, so that it can be used for different tasks.

To

develop the best possible hardware architecture for an energy-efficient AI

system, both an optimization of the hardware and an efficient architecture of

the neural layers are necessary. Here, a hybrid, two-way optimization promises

the best energy efficiency with the required (classification) performance. To

this end, POMAA is aiming for two optimizations in one loop. Based on an

initial analysis of the underlying data and the task of the competition, an

automated generation and selection of NN architectures is performed. These

architectures can vary greatly in the layers (type, number of neurons, etc.)

and provide the required classification performance for the data with a minimum

number of parameters. In certain cases NN layers can be replaced by

signal/preprocessing methods in this phase of ML design. This set of

architectures is synthesized and their energy efficiency and load profiles are

determined by simulation. Based on these results, a new objective function is

automatically adapted and optimized with ML methods, which are especially designed

to evaluate expensive objective functions in order to optimize and

re-synthesize new architectures. This process is run through until the lowest

possible energy consumption has been achieved.