Neuromorphic Hardware learns to learn
Hyperparameters and learning algorithms for neuromorphic hardware are usually
chosen by hand to suit a particular task. In contrast, networks of neurons in
the brain were optimized through extensive evolutionary and developmental
processes to work well on a range of computing and learning tasks.
Occasionally this process has been emulated through genetic algorithms, but
these require themselves hand-design of their details and tend to provide a
limited range of improvements. We employ instead other powerful gradient-free
optimization tools, such as cross-entropy methods and evolutionary
strategies, in order to port the function of biological optimization
processes to neuromorphic hardware. As an example, we show these optimization
algorithms enable neuromorphic agents to learn very efficiently from rewards.
In particular, meta-plasticity, i.e., the optimization of the learning rule
which they use, substantially enhances reward-based learning capability of
the hardware. In addition, we demonstrate for the first time
Learning-to-Learn benefits from such hardware, in particular, the capability
to extract abstract knowledge from prior learning experiences that speeds up
the learning of new but related tasks. Learning-to-Learn is especially suited
for accelerated neuromorphic hardware, since it makes it feasible to carry
out the required very large number of network computations.
Reference: T. Bohnstingl, F. Scherr, C. Pehle, K. Meier, and W. Maass.
Neuromorphic hardware learns to learn.
Frontiers in Neuroscience, 13:483, 2019.